topics for research proposal in software engineering

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What are Software Engineer Research Topics?

Software engineer research topics are areas of exploration and study in the rapidly evolving field of software engineering. These research topics include various software development approaches, quality of software, testing of software, maintenance of software, security measures for software, machine learning models in software engineering, DevOps, and architecture of software. Each of these software engineer research topics has distinct problems and opportunities for software engineers to investigate and make major contributions to the field. In short, research topics for software engineering provide possibilities for software engineers to investigate new technologies, approaches, and strategies for developing and managing complex software systems.

For example, research on agile software development could identify the benefits and drawbacks of using agile methodology, as well as develop new techniques for effectively implementing agile practices. Software testing research may explore new testing procedures and tools, as well as assess the efficacy of existing ones. Software quality research may investigate the elements that influence software quality and develop approaches for enhancing software system quality and minimizing the faults and errors. Software metrics are quantitative measures that are used to assess the quality, maintainability, and performance of software.

The research papers on software engineering topics in this specific area could identify novel measures for evaluating software systems or techniques for using metrics to improve the quality of software. The practice of integrating code changes into a common repository and pushing code changes to production in small, periodic batches is known as continuous integration and deployment (CI/CD). This research could investigate the best practices for establishing CI/CD or developing tools and approaches for automating the entire CI/CD process.

2. Natural Language Processing

Multimodality

Multimodality in Natural Language Processing (NLP) is one of the appealing research ideas for software engineering at the nexus of computer vision, speech recognition, and NLP. The ability of machines to comprehend and generate language from many modalities, such as text, speech, pictures, and video, is referred to as multimodal NLP. The goal of multimodal NLP is to develop systems that can learn from and interpret human communication across several modalities, allowing them to engage with humans in more organic and intuitive ways.

The building of conversational agents or chatbots that can understand and create responses using several modalities is one example of multimodal NLP in action. These agents can analyze text input, voice input, and visual clues to provide more precise and relevant responses, allowing users to have a more natural and seamless conversational experience. Furthermore, multimodal NLP can be used to enhance language translation systems, allowing them to more accurately and effectively translate text, speech, and visual content.

The development of multimodal NLP systems must take efficiency into account. as multimodal NLP systems require significant computing power to process and integrate information from multiple modalities, optimizing their efficiency is critical to ensuring that they can operate in real-time and provide users with accurate and timely responses. Developing algorithms that can efficiently evaluate and integrate input from several modalities is one method for improving the efficiency of multimodal NLP systems.

Overall, efficiency is a critical factor in the design of multimodal NLP systems. Researchers can increase the speed, precision, and scalability of these systems by inventing efficient algorithms, pre-processing approaches, and hardware architectures, allowing them to run successfully and offer real-time replies to consumers. Software Engineering training will help you level up your career and gear up to land you a job in the top product companies as a skilled Software Engineer.

3. Applications of Data Mining in Software Engineering

Mining Software Engineering Data

The mining of software engineering data is one of the significant research paper topics for software engineering, involving the application of data mining techniques to extract insights from enormous datasets that are generated during software development processes. The purpose of mining software engineering data is to uncover patterns, trends, and various relationships that can inform software development practices, increase software product quality, and improve software development process efficiency.

Mining software engineering data, despite its potential benefits, has various obstacles, including the quality of data, scalability, and privacy of data. Continuous research in this area is required to develop more effective data mining techniques and tools, as well as methods for ensuring data privacy and security, to address these challenges. By tackling these issues, mining software engineering data can continue to promote many positive aspects in software development practices and the overall quality of product.

Clustering and Text Mining

Clustering is a data mining approach that is used to group comparable items or data points based on their features or characteristics. Clustering can be used to detect patterns and correlations between different components of software, such as classes, methods, and modules, in the context of software engineering data.

On the other hand, text mining is a method of data mining that is used to extract valuable information from unstructured text data such as software manuals, code comments, and bug reports. Text mining can be applied in the context of software engineering data to find patterns and trends in software development processes

4. Data Modeling

Data modeling is an important area of research paper topics in software engineering study, especially in the context of the design of databases and their management. It involves developing a conceptual model of the data that a system will need to store, organize, and manage, as well as establishing the relationships between various data pieces. One important goal of data modeling in software engineering research is to make sure that the database schema precisely matches the system's and its users' requirements. Working closely with stakeholders to understand their needs and identify the data items that are most essential to them is necessary.

5. Verification and Validation

Verification and validation are significant research project ideas for software engineering research because they help us to ensure that software systems are correctly built and suit the needs of their users. While most of the time, these terms are frequently used interchangeably, they refer to distinct stages of the software development process. The process of ensuring that a software system fits its specifications and needs is referred to as verification. This involves testing the system to confirm that it behaves as planned and satisfies the functional and performance specifications. In contrast, validation is the process of ensuring that a software system fulfils the needs of its users and stakeholders.

This includes ensuring that the system serves its intended function and meets the requirements of its users. Verification and validation are key components of the software development process in software engineering research. Researchers can help to improve the functionality and dependability of software systems, minimize the chance of faults and mistakes, and ultimately develop better software products for their consumers by verifying that software systems are designed correctly and that they satisfy the needs of their users.

6. Software Project Management

Software project management is an important component of software engineering research because it comprises the planning, organization, and control of resources and activities to guarantee that software projects are finished on time, within budget, and to the needed quality standards. One of the key purposes of software project management in research is to guarantee that the project's stakeholders, such as users, clients, and sponsors, are satisfied with their needs. This includes defining the project's requirements, scope, and goals, as well as identifying potential risks and restrictions to the project's success.

7. Software Quality

The quality of a software product is defined as how well it fits in with its criteria, how well it performs its intended functions, and meets the needs of its consumers. It includes features such as dependability, usability, maintainability, effectiveness, and security, among others. Software quality is a prominent and essential research topic in software engineering. Researchers are working to provide methodologies, strategies, and tools for evaluating and improving software quality, as well as forecasting and preventing software faults and defects. Overall, software quality research is a large and interdisciplinary field that combines computer science, engineering, and statistics. Its mission is to increase the reliability, accessibility, and overall quality of software products and systems, thereby benefiting both software developers and end consumers.

8. Ontology

Ontology is a formal specification of a conception of a domain used in computer science to allow knowledge sharing and reuse. Ontology is a popular and essential area of study in the context of software engineering research. The construction of ontologies for specific domains or application areas could be a research topic in ontology for software engineering. For example, a researcher may create an ontology for the field of e-commerce to give common knowledge and terminology to software developers as well as stakeholders in that domain. The integration of several ontologies is another intriguing study topic in ontology for software engineering. As the number of ontologies generated for various domains and applications grows, there is an increasing need to integrate them in order to enable interoperability and reuse.

9. Software Models

In general, a software model acts as an abstract representation of a software system or its components. Software models can be used to help software developers, different stakeholders, and users communicate more effectively, as well as to properly evaluate, design, test, and maintain software systems. The development and evaluation of modeling languages and notations is one research example connected to software models. Researchers, for example, may evaluate the usefulness and efficiency of various modeling languages, such as UML or BPMN, for various software development activities or domains.

Researchers could also look into using software models for software testing and verification. They may investigate how models might be used to produce test cases or to do model checking, a formal technique for ensuring the correctness of software systems. They may also examine the use of models for monitoring at runtime and software system adaptation.

The Software Development Life Cycle (SDLC) is a software engineering process for planning, designing, developing, testing, and deploying software systems. SDLC is an important research issue in software engineering since it is used to manage software projects and ensure the quality of the resultant software products by software developers and project managers. The development and evaluation of novel software development processes is one SDLC-related research topic. SDLC research also includes the creation and evaluation of different software project management tools and practices.

Researchers may also check the implementation of SDLC in specific sectors or applications. They may, for example, investigate the use of SDLC in the development of systems that are more safety-critical, such as medical equipment or aviation systems, and develop new processes or tools to ensure the safety and reliability of these systems. They may also look into using SDLC to design software systems in new sectors like the Internet of Things or in blockchain technology.

Why is Software Engineering Required?

Software engineering is necessary because it gives a systematic way to developing, designing, and maintaining reliable, efficient, and scalable software. As software systems have become more complicated over time, software engineering has become a vital discipline to ensure that software is produced in a way that is fully compatible with end-user needs, reliable, and long-term maintainable.

When the cost of software development is considered, software engineering becomes even more important. Without a disciplined strategy, developing software can result in overinflated costs, delays, and a higher probability of errors that require costly adjustments later. Furthermore, software engineering can help reduce the long-term maintenance costs that occur by ensuring that software is designed to be easy to maintain and modify. This can save money in the long run by lowering the number of resources and time needed to make software changes as needed.

2. Scalability

Scalability is an essential factor in software development, especially for programs that have to manage enormous amounts of data or an increasing number of users. Software engineering provides a foundation for creating scalable software that can evolve over time. The capacity to deploy software to diverse contexts, such as cloud-based platforms or distributed systems, is another facet of scalability. Software engineering can assist in ensuring that software is built to be readily deployed and adjusted for various environments, resulting in increased flexibility and scalability.

3. Large Software

Developers can break down huge software systems into smaller, simpler parts using software engineering concepts, making the whole system easier to maintain. This can help to reduce the software's complexity and makes it easier to maintain the system over time. Furthermore, software engineering can aid in the development of large software systems in a modular fashion, with each module doing a specific function or set of functions. This makes it easier to push new features or functionality to the product without causing disruptions to the existing codebase.

4. Dynamic Nature

Developers can utilize software engineering techniques to create dynamic content that is modular and easily modifiable when user requirements change. This can enable adding new features or functionality to dynamic content easier without disturbing the existing codebase. Another factor to consider for dynamic content is security. Software engineering can assist in ensuring that dynamic content is generated in a secure manner that protects user data and information.

5. Better Quality Management

An organized method of quality management in software development is provided by software engineering. Developers may ensure that software is conceived, produced, and maintained in a way that fulfills quality requirements and provides value to users by adhering to software engineering principles. Requirement management is one component of quality management in software engineering. Testing and validation are another part of quality control in software engineering. Developers may verify that their software satisfies its requirements and is error-free by using an organized approach to testing.

In conclusion, the subject of software engineering provides a diverse set of research topics with the ability to progress the discipline while enhancing software development and maintenance procedures. This article has dived deep into various research topics in software engineering for masters and research topics for software engineering students such as software testing and validation, software security, artificial intelligence, Natural Language Processing, software project management, machine learning, Data Mining, etc. as research subjects. Software engineering researchers have an interesting chance to explore these and other research subjects and contribute to the development of creative solutions that can improve software quality, dependability, security, and scalability.

Researchers may make important contributions to the area of software engineering and help tackle some of the most serious difficulties confronting software development and maintenance by staying updated with the latest research trends and technologies. As software grows more important in business and daily life, there is a greater demand for current research topics in software engineering into new software engineering processes and techniques. Software engineering researchers can assist in shaping the future of software creation and maintenance through their research, ensuring that software stays dependable, safe, reliable and efficient in an ever-changing technological context. KnowledgeHut’s top Programming certification course will help you leverage online programming courses from expert trainers.

Frequently Asked Questions (FAQs)

Ans: To find a research topic in software engineering, you can review recent papers and conference proceedings, talk to different experts in the field, and evaluate your own interests and experience. You can use a combination of these approaches.

Ans: You should study software development processes, various programming languages and their frameworks, software testing and quality assurance, software architecture, various design patterns that are currently being used, and software project management as a software engineering student.

Ans: Empirical research, experimental research, surveys, case studies, and literature reviews are all types of research in software engineering. Each sort of study has advantages and disadvantages, and the research method chosen is determined by the research objective, resources, and available data.

Eshaan Pandey

Eshaan is a Full Stack web developer skilled in MERN stack. He is a quick learner and has the ability to adapt quickly with respect to projects and technologies assigned to him. He has also worked previously on UI/UX web projects and delivered successfully. Eshaan has worked as an SDE Intern at Frazor for a span of 2 months. He has also worked as a Technical Blog Writer at KnowledgeHut upGrad writing articles on various technical topics.

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topics for research proposal in software engineering

150 Best Research Paper Topics For Software Engineering

Software Engineering is a branch which deals with the creation and improvement of software applications using specific methodologies and clearly defined scientific principles. When developing software products, certain procedures must be followed, the outcome of which is a reliable and reliable software product. Software is a collection of executable code for programs with associated libraries. Software that is designed to meet certain requirements is referred to as a Software Product . This is an excellent subject for a master's thesis, research, or project. There are a variety of topics within Software Engineering which will be useful to M.Tech and other students studying for their masters to write their software thesis.

What is the reason Software Engineering is required?

Software Engineering is necessary due to the frequent shifts in the requirements of users as well as the environment. Through yourch and thesis, you will learn more about the significance of Software Engineering. Here are some other areas in software engineering that are needed:

Big Software: The massive dimension of software makes it necessary for the requirements in software engineering .
Scalability The concept of scaling Software Engineering makes it possible to increase the size of existing software rather than develop brand-new software.
Cost Price Software Engineering also cuts down the manufacturing cost that is incurred during software development.
The dynamic nature of Software - Software Engineering is a crucial factor when the need for new features is to be made in software in place, in the event that the nature of software is fluid.
Better Quality Management - Software Engineering can provide more efficient software development processes to provide superior-high-quality services .

Best Research Paper Topics on Software

Software Engineering Management Unified Software Development Process and Extreme ProgrammingThere are a lot of difficulties with managing the development of software for web-based applications and projects for systems integration that were completed in recent times.
The Blue Sky Software Consulting Company Analysis
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LabVIEW Software: Design Systems of Measurement
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Software-producing Firm Reducing Inventory
The link between the reduction in inventory levels and the number of orders is evident. An organization that produces software may think of increasing the amount of software to a lower level.
Moet Hennessy - Louis Vuitton: Enterprise Software
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Virtualization and Software-Defined Networking
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Computer Hardware and Software Components
Computers that were developed at the time of the 40s of 1940 have evolved into complex machines that require software and hardware for their operation.
Applications, Software and System Development
The usage the Microsoft Office applications greatly enhance productivity in the classroom as well as at work and during everyday activities at home.
PeopleSoft Inc.'s Software Architecture and Design
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Co-operative Banking Group's Enterprise Software
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Software Testing: Manual and Automated Web-Application Testing Tools
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JDA Software Company's Services
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Data Management, Networking and Enterprise Software
Enterprise software is typically developed "in-house" and thus has an inflated cost when contrasted to purchasing the software from another firm.
Software Workshops and Seminars Reflections
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The Various Enterprise Resource Planning Software Packages
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Explore Factors in IBM SPSS Statistical Software
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Split Variables in IBM SPSS Statistical Software
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Syntax Code Writing in Statistical Software
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Data Coding in Statistical Software
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Software Piracy at Kaspersky Cybersecurity Company
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Hotjar: Web Analytics Software Difference
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Avast Software: Company Analysis
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Project Failure, Project Planning Fundamentals, and Software Tools and Techniques for Alternative Scheduling
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Computer Elements such as Hardware and Software
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Review of a New Framework for Software Reliability Measurement
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Good Software Research Topics & Essay Examples

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A task management software plan's risk management strategy
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Computer Software Development and Reality Shows
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Scrum - Software Development Process
Digital systems and computerized systems have brought life to many areas. Scrum is a process for software development that guarantees high quality and efficiency.
Distribution of Anti-Virus Software
Numerous new threats are reported every fortnight. Cyberattacks, viruses, and other cyber-related threats are becoming an issue.
Marketing Plan: Innovative Type of Software Product
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Marketing System of Sakhr Software Co
The principal objective of this paper is to examine the marketing process in the same type of organization, like Sakhr Software Co.
Managing Information of Sakhr Software Co
This paper will examine the ideas of managing information for Sakhr Software, which is a well-known language software firm.
CRM Software in Amazon: Gains
The software for managing customers that Amazon.com developed is, from the beginning, one of the latest technology.
Neurofeedback Software and Technology Comparison
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PeopleSoft Software and HR.net Enterprise Software
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Business Applications: Revelation HelpDesk by Yellow Fish Software
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3D signal editing methods and editing software for stereoscopic movies
3D editing for movies is one of the newest trends and is among the most complex processes in the modern film industry.
ERP Software in Inventory Management
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The Capabilities of Compiere Software and How Well It Fits Into Different Industries
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Software Tools for Qualitative Research
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Data Scientist and Software Development
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IPR Violations in Software Development
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Health IT: Epic Software Analysis
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Agile Software Development Process
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Project Management Software and Tools Comparison
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Visually impaired people: challenges in Assistive Technology Software
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WBS completion and software project management
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International Software Development's Ethical Challenges: User-Useful Software
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Innovative Software to Blog About

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Benefits and Drawbacks of Agile Software Development Techniques
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The use of agile methodologies in the development of software contributes to the efficiency of work and efficiency of performance.
Large Scale Software Development
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Software Development: Creating a Prototype
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Software Engineering and Methodologies
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Information System Hardware and Software
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Software Development Project Using Agile Methods
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Antivirus Software Ensuring Security Online
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Britam Insurance Company's Sales and Marketing Management Software
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Software Programs: Adobe Illustrator
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Strawberry Business: Software Project Management
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Value of Salesforce Software Using VRIO Model
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Agile software development, as well as popular variations like Scrum, are the foundation for the work of a variety of testers and developers. No matter what team or method you're currently using, you can get expert guidance on process structure and the skills required to use Lean, Agile, DevOps, Waterfall and more to help you implement it for your business.

Most Interesting Software Research Titles

What Are the Essential Attributes of Good Software?
How Computer Software Can Be Used as a Tool for Education
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Online National Polling Software Requirements Specification
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Intellectual Property Rights, Innovation and Software Technologies
Software Piracy and the Canadian Piracy Act
For the development of software projects, agile methodologies and their Waterscrumfall derivative are used.
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How Software Can Support Academic Librarians' Changing Role
Using the Untangle Software to Overcome Obstacles for Small Businesses
By employing travel portal software, online booking sales will increase.
Analysis of Network Externality and Commercial Software Piracy
Accounting Software and Business Solutions
Analysis of Key Issues and Effects Relating to International Software Piracy
The Distinction Between Computer Science and Software Engineering
Modulation: Computer Software and Unknown Music Virus
Math Software for High School Students with Disabilities
Keyboarding Software Packages: Analysis and Purchase Recommended
Basic Software Development Life Cycle
India's Problems with Software Patents, Copyright, and Piracy
Why Has India Been Able to Build a Thriving Software Industry
Does Social Software Increase Labour Productivity
The Role of Open Source Software for Database Servers

Simple Software Essay Ideas

Human Capital and the Indian Software Industry
Input-Output Computer Windows Software
Business Software Development and Its Implementation
Evaluating Financial Management Software: Quicken Software
Which governance tools are important in Africa for combating software piracy?
Distinguish Between Proprietary Software and Off-The-Shelf
Does Social Software Support Service Innovation
Ambulatory Revenue Management Software
Difference Between Operating Systems and Application Software
Leading a Global Insurgency in the Software Sector are China and India
Call Accounting Software for Every Enterprise
Technology Standards for Software Outsourcing
The Importance of the Agile Approach for Software Development
Application Software: Publisher, Word, and Excel
Employee Monitoring Through Computer Software
Software Development Lifecycle and Testing's Importance
Tools for Global Conditional Policy to Combat Software Piracy
Software for Designing Solar Water Heating Systems
Open Source Software, Competition, and Potential Entry
Indian Software Industry: Gains are distorted and consolidated
Software Programs for Disabled Computer Users and Assistive Technology
Agile Software Architecture, Written by Christine Miyachi
Software Development: The Disadvantages of Agile Methods
Computer Software Technology for Early Childhood
Developing Test Automation Software Development

Easy Software Essay Topics

Growth Trends, Barriers, and Government Initiatives in the Indian Software Industry
How Does Enterprise Software Enable a Business to Use
Integrated Management Software the Processing of Information
Computer Software Training for Doctor's Office
Software Intellectual Property Rights and Venture Capitalist Access
Computer Science Software Specification
Software Projects and Student Software Risk Exposure
Why It Is Difficult to Create Software for Wireless Devices
Affiliate Tracking Software Your Payment Options
How Can Volkswagen Recover From the Cheating Issues It Had Because Illegal Software Was Installed?
Principles of Best Forensic Software Tool
The American Software Industry: A Historical Analysis
How Peripheral Developers Contribute to the Development of Open-Source Software
Agile Methodologies for Software Development
Key Macroeconomic Factors That Affect Software Industry
The Software Industry and India's Economic Development
Improving Customer Service Through Help Desk Software
Enterprise Resource Planning and Sap Software
Antivirus Software and Its Importance
Hardware and Software Used in Public Bank
The Effects of Computer Software Piracy on the Global Economy
Using the Winqsb Software in Critical Path Analysis
General Information About Interactive Multimedia-Based Educational Software
How Affiliate Tracking Software Can Benefit You
Computer Software and Recent Technologies

Frequently asked questions

What are the main topics of software engineering .

software development.

Introduction
Models and architecture for software development
Project management for software (SPM)
Software prerequisites
Testing and debugging software

What makes good research in software engineering ?

The most typical research strategy in software engineering is coming up with a novel method or methodology, validating it through analysis, or demonstrating its application through a case study;

What projects are good for software engineering ?

monitoring of Android tasks.
Analyzing attitudes to rate products
ATM with a fingerprint-based method.
a modern system for managing employees.
Using the AES technique for image encryption.
vote-by-fingerprint technology.
system for predicting the weather

What are the research methods in software engineering ?

We list and contrast the five categories of research methodology that, in our opinion, are most pertinent to software engineering: controlled experiments (including quasi-experiments); case studies (both exploratory and confirmatory); survey research; ethnographies; action research; and controlled experiments.

Is software engineering a research area ?

A relatively recent area of research, software engineering is derived from computer science. Its significance has been generally acknowledged by more and more academics in the field of computers throughout the course of six decades, from 1948 to the present, and it has developed into a vibrant and promising division of the computing profession.

Is software engineering easy ?

Yes, learning software engineering can be challenging at first, especially for those without programming or coding experience or any background in technology. However, numerous courses, tools, and other resources are available to assist with learning how to become a software engineer.

Who is the father of software engineering ?

The "father of software quality," Watts S. Humphrey, was an American software engineering pioneer who lived in Battle Creek, Michigan (U.S.) from July 4, 1927, to October 28, 2010.

What do you do in software engineering ?

roles and tasks for software engineers
creating and keeping up software systems.
testing and evaluating new software applications.
software speed and scalability optimization.
code creation and testing.
consulting with stakeholders such as clients, engineers, security experts, and others.

Which is better it or software engineering ?

IT support engineers cannot build sophisticated solutions, while software engineers can. In a word, they are in charge of creating and putting into use software. Knowing the distinctions makes it easier to choose the right individual to handle our tech-related problems.

Are junior software engineers in demand ?

Yes, there is a need for young coders.

Is software engineering going down ?

Software experts and software goods are oversaturating the job market for software engineers.

What degree do I need to be a software engineer ?

undergraduate degree

Can I be a software engineer without a degree ?

Many software developers lack a degree from a reputable university (or, in some circumstances, none at all).

How many years can a software engineer work ?

An engineer who wants to work in IT has a 15–20 year window.

How many hours do software engineers work ?

Software developers put in 8 to 9 hours each day, or 40 to 45 hours per week.

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: CompSci Research Topics

Algorithms & data structures
Artificial intelligence ( AI )
Computer networking
Database systems
Human-computer interaction
Information security (IS)
Software engineering
Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
An evaluation of machine learning algorithms used for recommender systems in streaming services
A review of approximation algorithm approaches for solving NP-hard problems
An analysis of parallel algorithms for high-performance computing of genomic data
The influence of data structures on optimal algorithm design and performance in Fintech
A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
A comparison of streaming algorithm performance for the detection of elephant flows
A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
Exploring the feasibility of applying information theory to feature extraction in retail datasets.
Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

Applying deep learning algorithms for speech recognition in speech-impaired children
A review of the impact of artificial intelligence on decision-making processes in stock valuation
An evaluation of reinforcement learning algorithms used in the production of video games
An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
An analysis of the ethical and social implications of artificial intelligence-based automated marking
The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
An examination of the use of artificial intelligence in orthopaedic surgery
The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

An analysis of the impact of 5G technology on internet penetration in rural Tanzania
Assessing the role of software-defined networking (SDN) in modern cloud-based computing
A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
Exploring the influence of cloud computing on security risks in fintech.
An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
An evaluation of the challenges and opportunities in 6G wireless network adoption
The role of network congestion control algorithms in improving network performance on streaming platforms
An analysis of network coding-based approaches for data security
Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Free Webinar: How To Find A Dissertation Research Topic

Topics & Ideas: Database Systems

An analysis of big data management systems and technologies used in B2B marketing
The impact of NoSQL databases on data management and analysis in smart cities
An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
Exploring the role of data warehousing and business intelligence in global consultancies
An analysis of the use of graph databases for data modelling and analysis in recommendation systems
The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
An examination of the challenges and opportunities of distributed databases in supply chain management
Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
An exploration of how artificial intelligence is changing human-computer interaction patterns in children
An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
Assessing the influence of virtual and augmented reality on consumer purchasing patterns
An examination of the use of gesture-based interfaces in architecture
Exploring the impact of ease of use in wearable technology on geriatric user
Evaluating the ramifications of gamification in the Metaverse
A systematic review of user experience (UX) design advances associated with Augmented Reality
A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Research Topic Kickstarter - Need Help Finding A Research Topic?

Topics & Ideas: Information Security

A bibliometric review of current trends in cryptography for secure communication
An analysis of secure multi-party computation protocols and their applications in cloud-based computing
An investigation of the security of blockchain technology in patient health record tracking
A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
A systematic review of secure data storage solutions used for cloud computing in the fintech industry
An analysis of intrusion detection and prevention systems used in the healthcare sector
Assessing security best practices for IoT devices in political offices
An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
A comparative study of digital signature schemes adoption in property transfers
An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

A study of agile software development methodologies and their impact on project success in pharmacology
Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
A study of the impact of DevOps practices on software development and delivery in the healthcare sector
An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
An investigation of software testing techniques and methodologies for subscription-based offerings
A review of software security practices and techniques for protecting against phishing attacks from social media
An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
Exploring the impact of software development outsourcing on project success in multinational contexts
An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

An array-based optimization framework for query processing and data analytics (Chen, 2021)
Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
Embedding constructural documentation in unit tests (Nassif, 2019)
PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
A state-based approach to context modeling and computing (Yue, 2019)
A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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Research topics and ideas about data science and big data analytics

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Software Engineer Research Paper Topics 2021: Top 5

Whether you’re studying in advance or you’re close to getting that Software Engineering degree, it’s crucial that you look for possible research paper topics in advance. This will help you have an advantage in your course.

First off, remember that software engineering revolves around tech development and improvement.

Hence, your research paper should have the same goal. It shouldn’t be too complex so that you can go through it smoothly. At the same time, it shouldn’t be too easy to the point that it can be looked up online.

Choosing can be a difficult task. Students are often choosing buy assignment from a professional writer because of the wrong topic choice. Thus, to help you land on the best topic for your needs, we have listed the top 5 software engineer research paper topics in the next sections.

Machine Learning

Machine learning is one of the most used research topics of software engineers. If you’re not yet familiar with this, it’s a field that revolves around producing programs that improve its algorithm on its own just by the use of existing data and experience.

Basically, the art of machine learning aims to make intelligent tools. Here, you will need to use various statistical methods for your computers’ algorithms. This somehow makes it a complex and long topic.

Even so, the good thing about the said field is it covers a lot of subtopics. These can include using machine learning for face spoof detection, iris detection, sentiment analysis technique, and likes. Usually, though, machine learning will go hand in hand with certain detection systems.

Artificial Intelligence

Artificial Intelligence is a much easier concept than machine learning. Note, though, that the latter is just another type of AI tool.

AI refers to the human-like intelligence integrated into machines and computer programs. Focusing on this will give you much more topics to write about. Since it’s present in a lot of fields like gaming, marketing, and even random automated tasks, you will have more materials to refer to.

Some things that you can write about in your paper include AI’s relationship with software engineering, robotics, and natural processing. You can also write about the different types of artificial intelligence tools for a more guided research paper.

Internet Of Things

Another topic that you can write about is the Internet of Things, or more commonly known as IoT . This refers to interconnected devices, machines, or even living beings as long as a network exists.

Writing about IoT will open a huge array of possibilities to write about. You can talk about whether the topic is a problem that needs additional solutions or improvements. At the same time, you will be able to talk about specific machine requirements since IoT works mainly with communication servers.

In addition, the concept of the Internet of Things is also used in several fields like agriculture, e-commerce, and medicine. Because of this, you can rest assured that you won’t run out of things to talk about or refer to.

Software Development Models

Next up, we have software development models. If you want to write about a research paper(or maybe you decided to purchase custom research paper ?) relating to how one can start building an app or software, then using software development models as a topic is a good choice.

Here, you can choose to write about what the concept is or delve deeper into its different types. You can look into the Waterfall Model, V-Model, Incremental, RAD, Agile, Iterative, Spiral, and Prototype. You can choose either one or all of the models and then relate them to software engineering.

Clone Management

One of the most important elements in software engineering is the clone base. Hence, using this as a research topic will help you stay relevant to your course and its needs. In particular, you can focus on clone management.

Clone management is a task that revolves around ensuring that a database is free from error and duplicated codes. What makes this a good topic is its materials are still limited in the field of software engineering. This is compared to other clone-related topics. Hence, you can ensure a distinct topic for your paper.

To land on the best topic, take your interest into account. Look for the field that makes you curious and entertained. In this way, you can build motivation to actually know more about it, and not just for the sake of submitting.

Another good tip is to choose a unique topic. The ones we discussed above can be considered unique since they are some of the latest software-related topics. If you’re going to use a common one, then make sure that you put your own little twist to it. You can also consider seeing the topic in a different light.

Anyhow, your research paper, its grade, and overall quality will greatly depend on what you choose to write about.

Latest Thesis and Research Topics in Software Engineering

Unique software engineering research topics for students.

more software engineers are needed as a result of the growing reliance on technology in both personal and professional spheres of life. Software engineering research topics are essential for solving complicated issues, increasing productivity, and fostering innovation. While software engineering is so important, it is equally difficult for students to get their degree in Software engineering.

Being said that many students struggle to keep up academically because software engineering is one of the most desired degrees. The final year thesis or dissertation is the most challenging assignment; many students are on the edge of losing their minds over it. While writing a thesis is one duty, coming up with an original and creative software engineering research topic is the first and most challenging step. Students with their assignments and activities don’t have enough time or energy to build a topic that is exactly right for them, finding a topic that is feasible and corresponds with your interests requires a lot of effort.

However this issue can be resolved as our PhD experts can provide you with well researched software engineering dissertation topics . We have plenty of topics for you to choose from mentioned below, and even if you don’t find anything according to your interests here you can simply contact us and request your topics according to your requirements and our experts will get you a tailored software engineering thesis topic.

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List of Free Software Engineering Research Topics

An analysis of the undertaking of good outcome factors and difficulties in software engineering projects:, how “the research guardian” can help you a lot.

Our top thesis writing experts are available 24/7 to assist you the right university projects. Whether its critical literature reviews to complete your PhD. or Master Levels thesis.

Automated software testing and quality control:

The study aims to improve programming testing and quality control through the execution of mechanized testing methods.

Objectives:

To efficiently detect software defeat and ensure complete test coverage, create an automated testing framework.
To determine which automated testing frameworks and tools are best suited to software development.
To analyze key metrics, and contrast them with the manual testing method to investigate the effects.

Impact of DevOps practices on software development:

The study aims to examine how DevOps practices affect software development productivity and efficiency.

To encourage cross-functional teams to collaborate, share information, and jointly advanced the development process.
To automate testing procedures like unit root tests, integration tests, and regression tests.
To change the activities for quality assurance and testing in the development process.

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Role of upgrading software security to enhance protection:

The aim of upgrading programming security through weakness identification and enhancing protection from possible breach

To find security flaws and weaknesses early on, employ, methods like vulnerability scanning, code reviews, and penetration testing.
To reduce the likelihood of being exploited, establish a procedure for resolving vulnerabilities as soon as possible.
To provide extensive security awareness and training programs, an organization can foster a security-conscious culture.

Adoption and effectiveness of continuous development:

The study aims to identify how effectively software engineering can be used for continuous development along with integration practices

To determine the benefit of implementing continuous deployment practices in numbers.
To evaluate the effect of computerizing the arrangement cycle, including code joining, testing, and delivery to the executive.
To analyze the impact of continuous integration practices on software development lifecycle enhancement.
To analyze how team communication and collaboration are affected by adopting software engineering practices and continuous development.

Looking For Customize Thesis Topics?

Take a review of different varieties of thesis topics and samples from our website TheResearchGuardian.com on multiple subjects for every educational level.

Planning and assess client-driven approaches in software programming:

The study aims to plan and assess client driven approaches to programing necessities and designing.

To identify the beneficial client-driven approaches necessary for programming and designing.
To ensure the successful implementation of these approaches in an organization.
To investigate the outcomes of these approaches in the success or failure of an organization.

Analyzing software metrics and their applications:

The study aims to analyze software metrics and their application to predictive software quality assurance.

To evaluate a comprehensive set of software metrics that can shed light on software product quality.
To create predictive models that make use of the software metrics that have been identified to predict potential risk and quality issues.
To compare the predictions made by the predictive models to actual software quality outcomes.

Applying Block chain Innovation:

The study aims to investigate how the distinctive characteristics of Block chain technology can be used to enhance software development and deployment process

To assess the potential use cases and advantages of coordinating block chain innovation into the product advancement lifecycle.
To investigate the application of block chain for transparent deployment histories, and decentralized package management.
To influence block chain’s straightforwardness to work with reviewing and consistence process in programming advancement.

Investigation of augmented and Virtual Reality into Software Engineering Methods and Tools:

The study aims to deeply analyse the integration of Augmented and Virtual Reality into Software Engineering Methods and tools to enhance the efficiency

To measure the impact of the integration of AR and VR technologies on software engineering
To examine the practical and technical obstacles to incorporate to incorporating augmented reality and virtual reality into existing software engineering techniques and tools.
To analyze existing frameworks and solution that make it possible to integrate AR and VR Software.

Complete Solution of All Your Hectic Thesis Papers

Our Expert online thesis writers are qualified and have expertise in almost all subject areas. This gives us an edge and we can help a lot of students who are struggling. Having a PhD expert in Software engineering gives us an advantage as we can help students looking for research topics in software engineering for masters, and then further help them with their research proposals and complete thesis.

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Software Engineering & Evolution

Software engineering is the most technical part of technical computer science , focused on developing and applying systematic principles common to other kinds of engineering (like mechanical or electrical engineering) to development of software systems. In particular, it covers:

Software requirements , their elicitation, specification and verification
Software architecture , modelling and design
Software testing , quality and guarantees
Software construction , methodologies and processes
Software deployment , configuration and maintenance
Software project management , planning and audit
Software ethics , licensing and collaboration

Software evolution , in particular, is a branch of software engineering focused on studying existing software and not necessarily creating new one. It covers, among other topics:

Mining repositories of versioned source code for interesting facts about software history
Refactoring, restructuring and replatforming of software
Developing and calculating metrics on software artefacts
Automatically measuring quality of source code by detecting harmful constructs
Bug fixing and other forms of corrective maintenance
Automating and tool-supporting maintenance activities

A typical research project in software engineering involves an implementation of a software system at least up to a fully functioning prototype , performing a feasibility study and/or a user study . A typical software evolution project covers development of a software system that analyses or transforms another software system. Both often use methodologies from empirical software engineering .

Prerequisites

Basic knowledge about programming and design of software

Related courses

Model-Driven Engineering
Principles of Programming, Processes and Patterns
Software Evolution

Available Project Proposals

If you are interested in the general topic of Software Engineering and Evolution , or if have your own project idea related to the topic, please contact us directly. Alternatively, you can also work on one of the following concrete project proposals:

Supervisors: Iman Hemati Moghadam , Vadim Zaytsev .

Gaining insights into the patterns of previously applied refactorings can greatly enhance the accuracy of detecting applied refactorings. By utilizing machine learning algorithms, we can extract change patterns through model training on pre-existing refactorings. However, in order to achieve a highly accurate model, it is crucial to train it on a comprehensive and extensive dataset.

The first objective of this project is to create an automated mechanism that generates a dataset of refactorings applied in Java applications. Mauricio et al. [1] offer a dataset with millions of refactorings and an ML model trained on this dataset to recognize refactoring opportunities. The provided dataset includes class, method, and variable-level refactorings but lacks field-level refactorings. Furthermore, the dataset's refactorings are identified using an outdated version of RefactoringMiner [2], which may have missed some applied refactorings. Although RefactoringMiner has improved recall in its latest version, our new experiments [3] revealed still some applied refactorings not detected by its latest version. Consequently, the dataset by Mauricio et al. does not encompass all applied refactorings. Furthermore, in order to develop a model that can effectively identify applied refactoring, we need a more informative dataset that incorporates information from both the original and refactored versions of the program. Currently, the dataset provided by Mauricio et al. only includes information about the original version of the program. Therefore, we need to create the dataset from scratch. The most challenging aspect of this process lies in accurately assessing the validity of each refactoring included in the dataset.

Our approach involves employing RefactoringMiner [2] and RefDetect [3] to extract the refactorings that have been applied in Java applications within the dataset compiled by Mauricio et al. [1]. RefactoringMiner demonstrates high precision, ensuring that the refactorings identified by it can be considered valid. However, its recall rate is comparatively lower than that of RefDetect, which we will employ to identify refactoring instances missed by RefactoringMiner. Given RefDetect's strong recall performance, we are confident that any changes not recognized as refactorings by RefDetect can be classified as non-refactoring modifications. Nevertheless, it is imperative to verify changes identified by RefDetect as refactoring and detected as non-refactoring by RefactoringMiner. We call them ambiguous changes. Although it is not impossible to conduct a manual evaluation, it can be a time-consuming and error-prone task. Therefore, it is imperative to discover an automated approach for validating ambiguous changes. To address this, we propose exploring the possibility of automatically labelling the refactorings using a machine-learning algorithm or a combination of machine-learning and search-based techniques. Further information regarding the challenges encountered and the corresponding approaches proposed to address them is provided in a detailed description of the project.

After establishing the aforementioned dataset, the next objective of this project is to leverage the dataset to train a model that can proficiently recognize applied refactorings in Java applications or augment RefDetect [3] in making more informed determinations when identifying refactorings.

[1] Aniche, M., Maziero, E., Durelli, R., & Durelli, V. H. (2020). The effectiveness of supervised machine learning algorithms in predicting software refactoring . IEEE Transactions on Software Engineering, 48(4), 1432-1450.

[2] Tsantalis, N., Ketkar, A., & Dig, D. (2020). RefactoringMiner 2.0 . IEEE Transactions on Software Engineering, 48(3), 930-950.

[3] Moghadam, I. H., Cinnéide, M. Ó., Zarepour, F., & Jahanmir, M. A. (2021). RefDetect: A multi-language refactoring detection tool based on string alignment . IEEE Access, 9, 86698-86727.

There exist many pre-trained code embedding methods, and determining the most resilient approach in the presence of modifications has consistently intrigued researchers. Although certain investigations have examined and compared a selection of prevalent embedding models such as CodeBERT, Codex, Code2vec, and Code2src, their emphasis has predominantly centred around the generation of adversarial samples via the renaming of entities using Rename refactoring. In a few research works, however, code modifications were made using transformations like loop exchange, try-catch insertion, replacing switch statements with if, swapping unrelated statements, etc.

However, in the majority of software development activities where the pre-trained models are used (such as code completion, code understanding, bug detection and fixing and refactoring assistance, etc.), the modifications extend beyond simple changes. Therefore, it is necessary to evaluate the robustness of the existing pre-trained models on complex code modification. To accomplish this goal, as an initial step, we generate semantically equivalent code samples by applying refactorings such as Extract and Inline Method refactorings and subsequently evaluating the performance of different models. The findings from this research can offer invaluable insights for selecting a robust model for future projects, thereby enhancing decision-making processes.

Supervisor: Fernando Castor

The Rust programming language aims to make systems programming efficient and safe at the same time by helping developers build programs that are safe by construction. The language is statically typed and supports safe access to memory, without the need for a garbage collector or runtime system, with the help of its compiler. It also provides scoped concurrency while avoiding state sharing, with exit synchronization for groups of threads. According to the 2023 StackOverflow developers survey (https://survey.stackoverflow.co/2023/), it is the most admired technology for survey respondents and has been so for many years.

One thing that Rust does not have, though, is a specific mechanism for signaling and handling errors, differently from a number of popular programming languages, such as Java, C++, Swift, and Python. In Rust, unrecoverable errors are signaled by the panic() function. Computations that may produce errors are represented by values of Result, an enumerated type that encapsulates both correct and erroneous results. These values are just regular Rust values and are not propagated automatically, differently from exceptions in other languages. On the one hand, this means that Rust avoids additional runtime infrastructure to perform stack unwinding during exception propagation. On the other hand, developers must explicitly worry about whether the output of a function is an error or not.

Previous work has shown that, in a number of languages, developers give less attention to code that handles errors than to other parts of the code. They test error handling code less [1], capture errors without doing anything with them [2,3], capture the incorrect errors [4], fail to account for potential errors [5], and sometimes simply do not use the language's error handling mechanism [6]. Problemas with error handling are commonplace even in languages that do not include specific mechanisms for handling errors [7].

In this project we would like to address a high-level research question:

RQ How do Rust programmers handle errors? How much code is dedicated to that?

This question can be decomposed in a number of more specific research questions:

RQ1 How are errors typically handled in Rust programs? Are they often ignored, as in other languages?

RQ1 .1 Is it common to have long sequences (in terms of method calls) where we have chained error handling, the kind of thing that would not be there with exception propagation?

RQ2 What do developers think of Rust error handling? Is it better than C? Better than exceptions?

RQ3 Do automated tests for Rust programs test exceptional paths?

RQ4 What are error handling bugs in Rust like?

RQ5 How are errors handled in the presence of scoped concurrency?

[1] Felipe Ebert, Fernando Castor, Alexander Serebrenik. An exploratory study on exception handling bugs in Java programs. J. Syst. Softw. 106: 82-101 (2015)

[2] Nathan Cassee, Gustavo Pinto, Fernando Castor, Alexander Serebrenik. How swift developers handle errors. MSR 2018: 292-302

[3] Bruno Cabral, Paulo Marques. Exception Handling: A Field Study in Java and .NET. ECOOP 2007: 151-175

[4] Nélio Cacho, Thiago César, Thomas Filipe, Eliezio Soares, Arthur Cassio, Rafael Souza, Israel García, Eiji Adachi Barbosa, Alessandro Garcia. Trading robustness for maintainability: an empirical study of evolving c# programs. ICSE 2014: 584-595

[5] Juliana Oliveira, Deise Borges, Thaisa Silva, Nélio Cacho, Fernando Castor. Do android developers neglect error handling? a maintenance-Centric study on the relationship between android abstractions and uncaught exceptions. J. Syst. Softw. 136: 1-18 (2018)

[6] Rodrigo Bonifácio, Fausto Carvalho, Guilherme Novaes Ramos, Uirá Kulesza, Roberta Coelho. The use of C++ exception handling constructs: A comprehensive study. SCAM 2015: 21-30

[7] Magiel Bruntink, Arie van Deursen, Tom Tourwé. Discovering faults in idiom-based exception handling. ICSE 2006: 242-251

Supervisors: Uraz Odyurt , Vadim Zaytsev .

The role of simulation and synthetic data generation for High-Energy Physics (HEP) research is profound. While there are physics-accurate simulation frameworks available to provide the most realistic data syntheses, these tools are computationally demanding. Additionally, the output from physics-accurate simulations is hard to comprehend, hard to manipulate and difficult to work with, as the data is rather close to the real case. These simulations consider accurate models of real-world detectors, which is another limitation.

Parametric and complexity-aware simulation frameworks on the other hand, can redefine the complexity space in drastically simplified terms and generate complexity-reduced data sets. It is also possible to consider a variety of detector models for these simulations. The applications of complexity-reduced simulations and data are numerous. We will be focusing on the role of such data as an enabler for Machine Learning (ML) model design research.

This project aims to extend an existing REDVID simulation framework through addition of new features.

( Read the full project description )

Nowadays, thanks to the rapid proliferation of mobile phones, tablets, and unwired devices in general, energy efficiency is becoming a key software design consideration where the energy consumption is closely related to battery lifetime. It is also of increasing interest in the non-mobile arena, such as data centers and desktop environments. Energy-efficient solutions are highly sought after across the compute stack, with more established results through innovations in hardware/architecture [1,2], operating systems [3], and runtime systems [4]. In recent years, there is a growing interest in studying energy consumption from higher layers of the compute stack and most of these studies focus on application software [5,6,7,8]. These approaches complement prior hardware/OS-centric solutions, so that improvements at the hardware/OS level are not cancelled out at the application level, e.g., due to misuses of language/library/application features.

We believe a critical dimension to further improve energy efficiency of software systems is to understand how software developers think. The needs of developers and the challenges they face may help energy-efficiency researchers stay focused on the real-world problems. The collective wisdom shared by developers may serve as a practical guide for future energy- aware and energy-efficient software development. The conceptually incorrect views they hold may inspire educators to develop more state-of-the-art curricula.

The goal of this work is to obtain a deeper understanding of (i) whether application programmers are interested in software energy consumption, and, if so, (ii) how they are dealing with energy consumption issues. Specifically, the questions we are trying to answer are:

RQ1 What are the distinctive characteristics of energy-related questions?

RQ2 What are the most common energy-related problems faced by software developers?

RQ3 According to developers, what are the main causes for software energy consumption?

RQ4 What solutions do developers employ or recommend to save energy?

We leverage data from StackOverflow, the most popular software development Q&A website, and on issues reported in issue trackers of real open source software projects to answer these questions.

[1] L. Bircher and L. John. Analysis of dynamic power management on multi-core processors. In ICS, 2008.

[2] A. Iyer and D. Marculescu. Power efficiency of voltage scaling in multiple clock, multiple voltage cores. In ICCAD, 2002.

[3] R. Ge, X. Feng, W. chun Feng, and K. Cameron. Cpu miser: A performance-directed, run-time system for power-aware clusters. In ICPP, 2007.

[4] H. Ribic and Y. D. Liu. Energy-efficient work-stealing language runtimes. In ASPLOS, 2014.

[5] Wellington Oliveira, Bernardo Moraes, Fernando Castor, João Paulo Fernandes. Analyzing the Resource Usage Overhead of Mobile App Development Frameworks. EASE 2023: 152-161

[6] Wellington Oliveira, Renato Oliveira, Fernando Castor, Gustavo Pinto, João Paulo Fernandes. Improving energy-efficiency by recommending Java collections. Empir. Softw. Eng. 26(3): 55 (2021)

[7] Ding Li, Shuai Hao, William G. J. Halfond, Ramesh Govindan. Calculating source line level energy information for Android applications. ISSTA 2013: 78-89

[8] Stefanos Georgiou, Maria Kechagia, Tushar Sharma, Federica Sarro, Ying Zou. Green AI: Do Deep Learning Frameworks Have Different Costs? ICSE 2022: 1082-1094

Supervisors: Iman Hemati Moghadam , Vadim Zaytsev .

Metric-based approaches are the most used technique in identifying refactoring opportunities by calculating a particular set of code metrics and applying predefined thresholds for each code metric. However, this approach faces three key challenges. Firstly, the lack of a universally accepted methodology for selecting metrics. Secondly, the absence of standardized definitions for code metrics. Furthermore, the accuracy of the metric-based approaches is heavily dependent on choosing appropriate thresholds.

Machine learning techniques provide effective solutions to overcome the aforementioned limitations. In certain approaches, the code snippet is transformed into a vector of source code metrics, which is then used to train ML classifiers. However, this approach fails to preserve the semantics and structure of the code. Conversely, pre-trained language models like CodeBERT and Codex have been trained in vast amounts of code and have learned to understand the syntax, semantics, and context of programming languages. This valuable knowledge can be transferred in recovering refactoring opportunities, and enable us to perform well even with limited task-specific training data.

The primary objective of this project is to improve the performance of a pre-trained language model (e.g., Codex, CodeBERT, etc.) in identifying opportunities for refactoring within Java applications. To accomplish this, we will start by fine-tuning the chosen language model using Mauricio et al.'s existing dataset [1]. Then, we will utilize the enhanced model to identify potential refactoring opportunities in Java applications. To assess the effectiveness of our approach, we will compare its accuracy with the model proposed by Mauricio et al. [1].

While current machine learning models, such as one developed by Mauricio et al. [1], can identify opportunities for refactorings, they do not provide specific guidance on how to apply the refactorings. For instance, while the existing models can recognize the necessity of applying an Extract Method refactoring to a given method, they do not specify which portion of the method should be extracted (e.g., the initial token and end token).

The goal of this project is to develop algorithms that not only identify parts of code that require refactorings but also recommend appropriate refactoring techniques. The focus of this project will be on two commonly used refactoring types: Extract Method and Inline Method. These two refactoring types are frequently employed by developers, and manually applying them is challenging.

Our proposed approach involves leveraging state-of-the-art deep learning models specifically designed for code analysis, including GraphCodeBERT, CuBERT, CodeGPT, Codex, etc. These models have undergone pre-training on extensive datasets, enabling them to comprehend the syntax, semantics, and context of programming languages effectively. Moreover, they offer the potential for fine-tuning based on our approach. However, before fine-tuning the chosen model, we need to construct a dataset that comprises both pre-refactoring and post-refactoring code snippets. To accomplish this, we will extend Mauricio et al.'s dataset [1], which encompasses thousands of Java applications. The resulting dataset comprises code snippets both before and after the application of refactorings, (i.e., Extract Method and Inline Method refactorings), and will be employed in the fine-tuning process of the selected pre-trained model.

Frontiers in Computer Science
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Software Engineering and Intelligent Systems

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Software engineering and intelligent systems are two dynamic and interrelated fields that have witnessed significant advancements and transformations in recent years. The convergence of these domains has led to the development of innovative applications and solutions that are shaping various industries, from healthcare and finance to transportation and manufacturing. This Research Topic aims to serve as a platform for researchers and practitioners to share their insights, findings, and innovations in this exciting and rapidly evolving intersection of software engineering and intelligent systems. Intelligent systems, often powered by artificial intelligence (AI) and machine learning (ML), have seen remarkable growth and adoption in various domains. We invite researchers and practitioners from academia and industry to contribute their original work to this article collection. Topics of interest include but are not limited to the following: - AI in Software Development; - Adaptive and Self-Learning Software; - Data Science and Big Data in Software Engineering; - Software Engineering Methodologies; - Artificial Intelligence and Machine Learning; - Case Studies and Industry Applications. Extended versions of work presented at the “Austrian Conference on Research at Universities of Applied Sciences (FHK Konferenz 2024)” are particularly welcome to submit to this journal. Extended versions should have at least 30% novel content.

Keywords : Software Engineering, Artificial Intelligence, Intelligent Systems, Data Science, Machine Learning

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Identifying Non-Technical Skill Gaps in Software Engineering Education: What Experts Expect But Students Don’t Learn

As the importance of non-technical skills in the software engineering industry increases, the skill sets of graduates match less and less with industry expectations. A growing body of research exists that attempts to identify this skill gap. However, only few so far explicitly compare opinions of the industry with what is currently being taught in academia. By aggregating data from three previous works, we identify the three biggest non-technical skill gaps between industry and academia for the field of software engineering: devoting oneself to continuous learning , being creative by approaching a problem from different angles , and thinking in a solution-oriented way by favoring outcome over ego . Eight follow-up interviews were conducted to further explore how the industry perceives these skill gaps, yielding 26 sub-themes grouped into six bigger themes: stimulating continuous learning , stimulating creativity , creative techniques , addressing the gap in education , skill requirements in industry , and the industry selection process . With this work, we hope to inspire educators to give the necessary attention to the uncovered skills, further mitigating the gap between the industry and the academic world.

Opportunities and Challenges in Code Search Tools

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.

Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines

A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this article, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.

Towards an Anatomy of Software Craftsmanship

Context: The concept of software craftsmanship has early roots in computing, and in 2009, the Manifesto for Software Craftsmanship was formulated as a reaction to how the Agile methods were practiced and taught. But software craftsmanship has seldom been studied from a software engineering perspective. Objective: The objective of this article is to systematize an anatomy of software craftsmanship through literature studies and a longitudinal case study. Method: We performed a snowballing literature review based on an initial set of nine papers, resulting in 18 papers and 11 books. We also performed a case study following seven years of software development of a product for the financial market, eliciting qualitative, and quantitative results. We used thematic coding to synthesize the results into categories. Results: The resulting anatomy is centered around four themes, containing 17 principles and 47 hierarchical practices connected to the principles. We present the identified practices based on the experiences gathered from the case study, triangulating with the literature results. Conclusion: We provide our systematically derived anatomy of software craftsmanship with the goal of inspiring more research into the principles and practices of software craftsmanship and how these relate to other principles within software engineering in general.

On the Reproducibility and Replicability of Deep Learning in Software Engineering

Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility —whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability —whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a different experimental setup. We observed that DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process, unlike classical supervised machine learning (ML) methods (e.g., random forest). This study aims to investigate the urgency and importance of reproducibility and replicability for DL studies on SE tasks. Method: In this study, we conducted a literature review on 147 DL studies recently published in 20 SE venues and 20 AI (Artificial Intelligence) venues to investigate these issues. We also re-ran four representative DL models in SE to investigate important factors that may strongly affect the reproducibility and replicability of a study. Results: Our statistics show the urgency of investigating these two factors in SE, where only 10.2% of the studies investigate any research question to show that their models can address at least one issue of replicability and/or reproducibility. More than 62.6% of the studies do not even share high-quality source code or complete data to support the reproducibility of their complex models. Meanwhile, our experimental results show the importance of reproducibility and replicability, where the reported performance of a DL model could not be reproduced for an unstable optimization process. Replicability could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data. Conclusion: It is urgent for the SE community to provide a long-lasting link to a high-quality reproduction package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.

Predictive Software Engineering: Transform Custom Software Development into Effective Business Solutions

The paper examines the principles of the Predictive Software Engineering (PSE) framework. The authors examine how PSE enables custom software development companies to offer transparent services and products while staying within the intended budget and a guaranteed budget. The paper will cover all 7 principles of PSE: (1) Meaningful Customer Care, (2) Transparent End-to-End Control, (3) Proven Productivity, (4) Efficient Distributed Teams, (5) Disciplined Agile Delivery Process, (6) Measurable Quality Management and Technical Debt Reduction, and (7) Sound Human Development.

Software—A New Open Access Journal on Software Engineering

Software (ISSN: 2674-113X) [...]

Improving bioinformatics software quality through incorporation of software engineering practices

Background Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software. Methodology A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software. Results The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers. Conclusions While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

Inter-team communication in large-scale co-located software engineering: a case study

AbstractLarge-scale software engineering is a collaborative effort where teams need to communicate to develop software products. Managers face the challenge of how to organise work to facilitate necessary communication between teams and individuals. This includes a range of decisions from distributing work over teams located in multiple buildings and sites, through work processes and tools for coordinating work, to softer issues including ensuring well-functioning teams. In this case study, we focus on inter-team communication by considering geographical, cognitive and psychological distances between teams, and factors and strategies that can affect this communication. Data was collected for ten test teams within a large development organisation, in two main phases: (1) measuring cognitive and psychological distance between teams using interactive posters, and (2) five focus group sessions where the obtained distance measurements were discussed. We present ten factors and five strategies, and how these relate to inter-team communication. We see three types of arenas that facilitate inter-team communication, namely physical, virtual and organisational arenas. Our findings can support managers in assessing and improving communication within large development organisations. In addition, the findings can provide insights into factors that may explain the challenges of scaling development organisations, in particular agile organisations that place a large emphasis on direct communication over written documentation.

Aligning Software Engineering and Artificial Intelligence With Transdisciplinary

Study examined AI and SE transdisciplinarity to find ways of aligning them to enable development of AI-SE transdisciplinary theory. Literature review and analysis method was used. The findings are AI and SE transdisciplinarity is tacit with islands within and between them that can be linked to accelerate their transdisciplinary orientation by codification, internally developing and externally borrowing and adapting transdisciplinary theories. Lack of theory has been identified as the major barrier toward towards maturing the two disciplines as engineering disciplines. Creating AI and SE transdisciplinary theory would contribute to maturing AI and SE engineering disciplines. Implications of study are transdisciplinary theory can support mode 2 and 3 AI and SE innovations; provide an alternative for maturing two disciplines as engineering disciplines. Study’s originality it’s first in SE, AI or their intersections.

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Offered MSc Thesis topics

See also our current list of projects on the Research page to get an idea of what is topical in our research. Another list of all our projects is also available in Tuhat, with responsible persons listed (you can ask them about potential thesis topics).

A more exhaustive list of topics from the department is available at CSM Master thesis topics (moodle).

General writing Instructions

We have written some instructions to help the students write their Master's theses, seminar papers and B.Sc. theses. Please, read the guide before starting your thesis work: Scientific Writing – Guide of the Empirical Software Engineering Research Group .

Master's Thesis Topics

Software engineering and technology are prevalent areas for thesis at the department, and many candidates ask for thesis topics every academic year. We do our best to accommodate the requests, but the applicants can smoothen the process by taking an active role in thinking about potential topics based on the themes presented below.

We provide guidance for selecting a suitable topic and the supervision and support needed to complete the work. Please contact Antti-Pekka Tuovinen or Tomi Männistö if you are interested. You can also contact the group members to ask about the subject areas they are working on.

Suppose you, as a student, are working in software development, processes, architecture or something related. In that case, there is a good chance of finding an interesting thesis topic that closely relates to your work. In such a case, the actual work often provides an excellent problem to investigate, propose or try out potential solutions for, or the case can act as a rich source of data about the practice of software development.

We also welcome companies to suggest potential topics for Master's thesis. The topics can be general, based on existing research, or they may require original research and problem-solving. We will help to evaluate and fine-tune the proposals. Depending on the topic, you may also need to be prepared to provide guidance and assistance during the thesis project.

Please contact Antti-Pekka Tuovinen or Tomi Männistö if you have an idea for an industrial thesis and need further information.

The listing below introduces our current research areas and potential topics for the thesis. Each topic has a short description and the names of the researchers working on the topic. Please contact them for more details about the research and thesis work. Note that you can also suggest and discuss other topics within the general area of software engineering research. We encourage creativity and student-centred insight in selecting and defining the topic.

Earlier theses

Some earlier MSc thesis titles below give some idea about the topics. You can try looking up more info from E-thesis , but note that it is up to the author if the actual thesis pdf is available online. Just search using the title (or part of it) in quotation marks. You can also go to the library in person and read all theses (even those without a public pdf) on a kiosk workstation (ask the staff if you need help).

Exploring study paths and study success in undergraduate Computer Science studies
EU:n tietosuoja-asetuksen GDPR:n vaikutus suomalaisissa pk-yrityksissä 2018-2020
Industrial Surveys on Software Testing Practices: A Literature Review
Laskennallisesti raskaan simulointiohjelmistokomponentin korvaaminen reaaliaikasovelluksessa koneoppimismenetelmällä
Web service monitoring tool development
Case study: identifying developer oriented features and capabilities of API developer portals
Documenting software architecture design decisions in continuous software development – a multivocal literature review
Elinikäinen oppiminen ohjelmistotuotannon ammattilaisen keskeisenä
Miten huoltovarmuus toteutuu Ylen verkkouutisissa?
Utilizing Clustering to Create New Industrial Classifications of Finnish Businesses: Design Science Approach
Smoke Testing Display Viewer 5
Modernizing usability and development with microservices
On the affect of psychological safety, team leader’s behaviour and team’s gender diversity on software team performance: A literature review
Lean software development and remote working during COVID-19 - a case study
Julkaisusyklin tihentämisen odotukset, haasteet ja ratkaisut
Software Development in the Fintech Industry: A Literature Review
Design of an automated pipeline to improve the process of cross-platform mobile building and deployment
Haasteet toimijamallin käytössä ohjelmistokehityksessä, systemaattinen kirjallisuuskatsaus
Light-weight method for detecting API breakages in microservice architectures
Kirjallisuuskatsaus ja tapaustutkimus API-hallinnasta mikropalveluarkkitehtuurissa
In-depth comparison of BDD testing frameworks for Java
Itseohjautuvan auton moraalikoneen kehittämisen haasteet
Towards secure software development at Neste - a case study
Etuuspohjaisen eläkejärjestelyn laskennan optimointi vakuutustenhallintajärjestelmässä
Internal software startup within a university – producing industry-ready graduates
Applying global software development approaches to building high-performing software teams
Systemaattinen kirjallisuuskatsaus lääkinnällisistä ohjelmistoista ja ketterästä ohjelmistokehityksestä
Matalan kynnyksen ohjelmointialustan hyödyntäminen projektinhalinnassa
Uncertainty Estimation with Calibrated Confidence Scores
Tool for grouping test log failures using string similarity algorithms
Design, Implementation, and Validation of a Uniform Control Interface for Drawing Robots with ROS2
Assuring Model Documentation in Continuous Machine Learning System Development
Verkkopalvelun saavutettavuuden arviointi ja kehittäminen ohjelmistotuoteyrityksessä
Methods for API Governance automation: managing interfaces in a microservice system
Improving Web Performance by Optimizing Cascading Style Sheets (CSS): Literature Review and Empirical Findings
Implementing continuous delivery for legacy software
Using ISO/IEC 29110 to Improve Software Testing in an Agile VSE
An Open-Source and Portable MLOps Pipeline for Continuous Training and Continuous Deployment
System-level testing with microservice architecture
Green in software engineering: tools, methods and practices for reducing the environmental impacts of software use – a literature review
Machine Learning Monitoring and Maintenance: A Multivocal Literature Review
Green in Software Engineering: A Systematic Literature Review
Comparison of Two Open Source Feature Stores for Explainable Machine Learning
Open-source tools for automatic generation of game content
Verkkosovelluskehysten energiankulutus: vertaileva tutkimus Blazor WebAssembly ja JavaScript
Infrastruktuuri koodina -toimintatavan tehostaminen
Geospatial DBSCAN Hyperparameter Optimization with a Novel Genetic Algorithm Method
Hybrid mobile development using Ionic framework
Correlation of Unit Test Code Coverage with Software Quality
Factors affecting productivity of software development teams and individual developers: A systematic literature review
Case study: Performance of JavaScript on server side
Reducing complexity of microservices with API-Saga
Organizing software architecture work in a multi-team, multi-project, agile environment
Cloud-based visual programming BIM design workflow
IT SIAM toimintojen kehitysprojekti
PhyloStreamer: A cloud focused application for integrating phylogenetic command-line tools into graphical interfaces
Evaluation of WebView Rendering Performance in the Context of React Native
A Thematic Review of Preventing Bias in Iterative AI Software Development
Adopting Machine Learning Pipeline in Existing Environment

Current topic areas of interest to the research group (see below for the details)

Open source-related topic areas in collaboration with daimler truck.

Open Chain: Developing the Journey to Open Chain Compliance at the example of Daimler Truck
How should an industrial company (for example, Daimler Truck) leverage open source software: Building a framework with different dimensions, from efficient governance to value in inner source and open source projects
How can an organization efficiently incentivize inner-source activities? (on different levels, culture, infrastructure, governance, regulations & commitments.)
How can an industrial organization leverage value from actively engaging in FOSS activities (especially on active creation and contribution)
How can spillovers help Industrial companies to educate the rare resources but also attract and retain talent? Ref: Gandal, N., Naftaliev, P., & Stettner, U. (2017). Following the code: spillovers and knowledge transfer. Review of Network Economics , 16 (3), 243-267. Abstract: Knowledge spillovers in Open Source Software (OSS) can occur via two channels: In the first channel, programmers take knowledge and experience gained from one OSS project they work on and employ it in another OSS project they work on. In the second channel, programmers reuse software code by taking code from an OSS project and employing it in another. We develop a methodology to measure software reuse in a large OSS network at the micro level and show that projects that reuse code from other projects have higher success. We also demonstrate knowledge spillovers from projects connected via common programmers.

If interested, contact Tomi Männistö for further information

Hybrid software development (TOPIC AREA)

The current pandemic has brought many, even radical, changes to almost all software companies and software development organizations. Especially the sudden moves to working from home (WFH) in March 2020 forced them to adapt and even rethink many software engineering practices in order to continue productive software development under the new constraints.

Now (December 2021), various hybrid ways of working appear to become the new "normal" for the software industry in general. For instance, many companies are offering flexible workplace arrangements (WFX).

This thesis theme aims to explore and possibly explain such changes in contemporary software engineering. Potential research questions include the following:

How has the COVID-19 pandemic affected different software engineering activities (negatively or positively)? What are the mechanisms?
What adaptations and countermeasures have different software organizations devised to cope with the challenges?
What could be learned from them for future hybrid software development processes, practices and tools?

Contact: Petri Kettunen

MLOps -- as a derivative of DevOps -- is about practice and tools for ML-based systems that technically enable iterative software engineering practice. We have several funded positions in the area of MLOps in our research projects (IMLE4 https://itea4.org/project/iml4e.html and AIGA https://ai-governance.eu/ ) that can be tailored to the interest of the applicant. For details, contact Mikko Raatikainen ( [email protected] ).

Digital Twin of Yourself

Digital twins are virtual world dynamic models of real-world physical objects. They originate from manufacturing domains. In such environments, they are utilized, for example, for predictive maintenance of equipment based on real-time machine data.

Recently the application domains of digital twins have broadened to cover living objects – especially human beings, for instance, in medical domains (so-called Human Digital Twins). In this thesis topic, the objective is to design a digital twin of yourself. The choice of the digital twin dynamic model is free, and so are the data inputs. One possibility could be, for instance, your real-life physical exercise data (e.g., from a heart-rate monitor). You could also consider your Citizen Digital Twin, following your study data and yourself as a lifelong learner.

Software engineering and climate change (TOPIC AREA)

Global climate change may have various impacts on future software engineering on the one hand, and software engineering may affect climate change directly or indirectly, positively or negatively on the other hand. All that opens up many potentially important research problems. Specific theses in this topic area could be, for instance, the following themes:

Green IT (e.g., engineering new software with energy-efficiency requirements in order to reduce or limit power consumption and consequently the carbon footprint)
Carbon neutrality goals of software companies (e.g., software development organizations decreasing physical travelling in order to reduce their greenhouse gas emissions)
Developing software products or services for measuring climate change-related factors

The thesis could be a literature review, an empirical case study or a scientific design work.

Life-long learning for the modern software engineering profession

Specific intended learning outcomes for computer science (software engineering) graduates are life-long learning skills. Such skills and capabilities are essential in modern industrial software engineering environments. Workplace learning is a vital part of most professional software development jobs. What are the necessary life-long learning skills exactly? Why are those skills and capabilities essential in different software organizations? How can they be learned and improved? How do software professionals learn in their workplaces? What particular skills will be more critical in the future? Why? This topic could be investigated by case studies in real-life software organizations. The specific research questions could be some of the above or possibly focused on particular skills (e.g., assessing one's own and the works of other software developers). Contact: Petri Kettunen

Software development in non-ICT contexts (TOPIC AREA)

Software technology is increasingly applied in non-ICT domains and environments (e.g., healthcare, financial sector, telecommunications systems, industrial automation). Such conditions bring up many considerations for effective and efficient software engineering, such as: What are the key characteristics of different use domains (e.g., complexity, reliability)? What is the scope of the particular software system? How are the software requirements engineered? What are the specific constraints (e.g., regulations) in different domains to be considered in software engineering? How to measure the success of software projects and products? What software development methods (e.g., agile) are applicable in different domains? Why/why not? What particular software-related competencies are needed (e.g., digitalization, IoT, cyber-physical systems)? This research problem could be investigated theoretically (literature study) and empirically in industrial case studies. The actual research questions could be some of the above or formulated individually. Contact: Petri Kettunen

Creatively self-adaptive software architectures (TOPIC AREA)

We have recently started exciting research in the intersection between the research fields of self-adaptive software and computational creativity, intending to develop novel software architectures that can creatively adapt themselves in unforeseen situations. This initiative is a new research collaboration between the Discovery Group of Prof. Hannu Toivonen and ESE. There are different options for thesis work with either of the groups. To get a better idea of the topic, see Linkola et al. 2017. Aspects of Self-awareness: An Anatomy of Metacreative Systems. http://computationalcreativity.net/iccc2017/ICCC_17_accepted_submissions/ICCC-1… Contact: Tomi Männistö

Continuous Experimentation (TOPIC AREA)

Software product and service companies need capabilities to evaluate their development decisions and customer and user value. Continuous experimentation, as an experiment-driven development approach, may reduce such development risks by iteratively testing product and service assumptions critical to the software's success. Experiment-driven development has been a crucial component of software development, especially in the last decade. Companies such as Microsoft, Facebook, Google, Amazon and many others often conduct experiments to base their development decisions on data collected from field usage. Contact: Tomi Männistö

Digitalization and digital transformations: impacts on software engineering and systems development (TOPIC AREA)

Digitalization is nowadays cross-cutting and inherent in most areas of businesses and organizations. Software is increasingly built-in and ubiquitous. Such trends and developments bring up many potential software research problems, such as: What does digitalization entail in different contexts? How should digitalization be taken into account in software development processes? What is the role of customer/user involvement in software-intensive systems development (e.g., digital services)? What are the key quality attributes? What new software engineering skills and competencies may be needed? What is the role of software (and IT) in general in different digital transformations (e.g., vs business process development)? How is digitalization related to traditional software engineering and computer science disciplines in different contexts? What aspects of software development and digital technologies are fundamentally new or different from the past? This research problem could be investigated theoretically (literature study) or empirically in industrial case studies. The actual research questions could be some of the above or formulated individually. Contact: Petri Kettunen

High-performing software teams (TOPIC AREA)

How is (high) performance defined and measured in software development (e.g., productivity)? Which factors affect it - positively or negatively - and how strongly (e.g., development tools, team composition)? Can we "build" high-performing software teams systematically, or do they merely emerge under certain favourable conditions? What are suitable organizational designs and environments for hosting and supporting such teams? See this link and this link for more info. Contact: Petri Kettunen

Software innovation (TOPIC AREA)

How are innovation and creativity taken into account in software development processes and methods (e.g., Agile)? What role do customer/user input and feedback play in software(-intensive) product creation (e.g., open innovation)? How to define and measure 'innovativeness' in software development? What makes software development organizations (more) innovative? See here for more about the topic. How can Open Data Software help innovation? Contact: Petri Kettunen

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110 Engineering Research Topics For Engineering Students!

Getting engineering topics for research or presentation is not an easy task. The reason is that the field of engineering is vast. Engineers seek to use scientific principles in the design and building of machines, structures, bridges, tunnels, etc.

Engineering as a discipline has a broad range of specialized fields such as chemical engineering, civil engineering, biomedical engineering, computer engineering, mechanical engineering, software engineering, and lots more! In all, engineering seeks to apply mathematics or science to solving problems.

110 Engineering Topic Ideas in Different Areas

Genetic engineering topics, mechanical engineering research topics, electrical engineering research topics, software engineering research topics, computer engineering research topics, biomedical engineering research topics, civil engineering topics, chemical engineering research topics, controversial engineering topics, aerospace engineering topics, industrial engineering topics, environmental engineering topics for research.

We understand how difficult and tiring it could be to get engineering research topics; hence this article contains a total of 110 interesting engineering topics covering all aspects of engineering. Ready to explore? Let’s begin right away!

Genetic engineering is the direct manipulation of the gene of an organism using biotechnology. Many controversies are surrounding this engineering field because of the fantastic potential feats it could achieve. Here are some genetic engineering topics that encompass essential areas of this field.

Can the human personality be altered through genetic engineering?
Genetic engineering: hope for children with intellectual disabilities?
Genetic engineering: the problems and perspectives.
Genetic engineering and the possibility of human cloning.
Genetic Engineering
The side effects of altering human personality
Immortalizing humans through genetic engineering
Addressing human deficiencies through genetic engineering

Mechanical engineering deals with the design and manufacture of physical or automated systems. These systems include power and energy systems, engines, compressors, kinematic chains, robotics, etc. Here are some impressive mechanical engineering topics that double as mechanical engineering thesis topics too.

A study of the compressed air technology used in cars.
The design of a motorized automatic wheelchair that can serve as a bed.
The why and how of designing stronger and lighter automobiles.
The design of an electronic-assisted hydraulic braking system.
Basics of Electronics Engineering
AC and DC motors and operations
Design and implementation of wind energy
Power lines and electricity distribution
Electromagnetic field and its applications
Generators and electric motors

Electrical engineering is a trendy and well-sought field that deals with the design and manufacture of different electrical and electronic systems. Electrical engineering encompasses power and electronics. The basic principle of digital technology and electricity are all given birth to in this field. From your lighting to computers and phones, everything runs based on electricity. Although finding topics in electrical engineering could be difficult, we have carefully selected four electrical engineering topics to give you a great head start in your research! or write research paper for me

A study on how temperature affects photovoltaic energy conversion.
The impact of solar charging stations on the power system.
Direct current power transmission and multiphase power transmission
Analysis of the power quality of the micro grid-connected power grid.
Solar power and inverters
Alternator and electric magnetic induction
AC to DC converters
Operational amplifiers and their circuits.

Software engineering deals with the application of engineering approaches systematically to develop software. This discipline overlaps with computer science and management science and is also a part of overall systems engineering. Here are some software engineering topics for your research!

The borderline between hardware and software in cloud computing.
Essential computer languages of the future.
Latest tendencies in augmented reality and virtual reality.
How algorithms improve test automation.
Essentials for designing a functional software
Software designing and cyber security
5 computer languages that will stand the test of time.
Getting software design right
Effects of malware on software operation.

Computer engineering integrates essential knowledge from the subfields of computer science, software engineering, and electronic engineering to develop computer hardware and software. Computer engineering applies various concepts to build complex structural models. Besides, we have completed researches in the information technology field and prepare great it thesis topics for you. Here are some computer engineering topics to help you with your research.

Biotechnology, medicine, and computer engineering.
Programs for computer-aided design (cad) of drug models.
More effective coding and information protection for multinational companies.
Why we will need greater ram in modern-day computers.
Analysis and computer-aided structure design
Pre-stressed concrete structures and variations
General computer analysis of structures
Machine foundation and structural design
Storage and industrial structures.

Biomedical engineering applies principles and design concepts from engineering to medicine and biology for diagnostic or therapeutic healthcare purposes. Here are some suggested biomedical engineering topics to carry out research on!

A study on how robots are changing health care.
Can human organs be replaced with implantable biomedical devices?
The advancement of brain implants.
The advancement of cell and tissue engineering for organ replacement.
Is planting human organs in machines safe?
Is it possible to plant biomedical devices insensitive to human organs?
How can biomedicine enhance the functioning of the human brain?
The pros and cons of organ replacement.

Civil engineering deals with the construction, design, and implementation of these designs into the physical space. It is also responsible for the preservation and maintenance of these constructions. Civil engineering spans projects like roads, buildings, bridges, airports, and sewage construction. Here are some civil engineering topics for your research!

Designing buildings and structures that withstand the impact of seismic waves.
Active noise control for buildings in very noisy places.
The intricacies of designing a blast-resistant building.
A compatible study of the effect of replacing cement with silica fume and fly ash.
Comparative study on fiber-reinforced concrete and other methods of concrete reinforcement.
Advanced construction techniques
Concrete repair and Structural Strengthening
Advanced earthquake resistant techniques
Hazardous waste management
Carbon fiber use in construction
Structural dynamics and seismic site characterization
Urban construction and design techniques

Chemical engineering transverses the operation and study of chemical compounds and their production. It also deals with the economic methods involved in converting raw chemicals to usable finished compounds. Chemical engineering applies subjects from various fields such as physics, chemistry, biology, and mathematics. It utilizes technology to carry out large-scale chemical processes. Here are some chemical engineering topics for you!

Capable wastewater treatment processes and technology.
Enhanced oil recovery with the aid of microorganisms.
Designing nanoparticle drug delivery systems for cancer chemotherapy.
Efficient extraction of hydrogen from the biomass.
Separation processes and thermodynamics
Heat, mass, and temperature
Industrial chemistry
Water splitting for hydrogen production
Mining and minerals
Hydrocarbon processes and compounds
Microfluidics and Nanofluidics.

Not everyone agrees on the same thing. Here are some engineering ethics topics and controversial engineering topics you can explore.

Are organic foods better than genetically modified foods?
Should genetically modified foods be used to solve hunger crises?
Self-driving cars: pros and cons.
Is mechanical reproduction ethical?
If robots and computers take over tasks, what will humans do?
Are electric cars really worth it?
Should human genetics be altered?
Will artificial intelligence replace humans in reality?

Aerospace engineering deals with the design, formation, and maintenance of aircraft, spacecraft, etc. It studies flight safety, fuel consumption, etc. Here are some aerospace engineering topics for you.

How the design of planes can help them weather the storms more efficiently.
Current techniques on flight plan optimization.
Methods of optimizing commercial aircraft trajectory
Application of artificial intelligence to capacity-demand.
Desalination of water
Designing safe planes
Mapping a new airline route
Understanding the structural design of planes.

Petroleum engineering encompasses everything hydrocarbon. It is the engineering field related to the activities, methods, processes, and adoptions taken to manufacture hydrocarbons. Hydrocarbon examples include natural gas and crude oil which can be processed to more refined forms to give new petrochemical products.

The effect of 3d printing on manufacturing processes.
How to make designs that fit resources and budget constraints.
The simulation and practice of emergency evacuation.
Workers ergonomics in industrial design.
Heat transfer process and material science
Drilling engineering and well formation
Material and energy flow computing
Well log analysis and testing
Natural gas research and industrial management

Manufacturing engineering is integral for the creation of materials and various tools. It has to do with the design, implementation, construction, and development of all the processes involved in product and material manufacture. Some useful production engineering topics are:

Harnessing freshwater as a source of energy
The design and development of carbon index measurement systems.
Process improvement techniques for the identification and removal of waste in industries.
An extensive study of biomedical waste management.
Optimization of transportation cost in raw material management
Improvement of facility layout using systematic planning
Facilities planning and design
Functional analysis and material modeling
Product design and marketing
Principles of metal formation and design.

So here we are! 110 engineering research paper topics in all major fields of engineering! Choose the ones you like best and feel free to contact our thesis writers for help. It’s time to save humanity!

Top 100+ Computer Engineering Project Topics [Updated]

Computer engineering projects offer a captivating blend of creativity and technical prowess, allowing enthusiasts to dive into a world where innovation meets functionality. Whether you’re fascinated by hardware design, software development, networking, or artificial intelligence, there’s a wide array of project topics to explore within the realm of computer engineering. In this blog, we’ll delve into some intriguing computer engineering project topics, catering to both beginners and seasoned enthusiasts alike.

What Is A CSE Project?

Table of Contents

A CSE project refers to a project within the field of Computer Science and Engineering (CSE). These projects involve the application of computer science principles and engineering techniques to develop software, hardware, or systems that solve real-world problems or advance technology.

CSE projects can range from developing new algorithms and programming languages to designing and building computer hardware, networking systems, software applications, or artificial intelligence systems.

They often require interdisciplinary knowledge and skills in areas such as programming, data structures, algorithms, software engineering, hardware design, networking, and more.

How Do I Start A CSE Project?

Starting a CSE (Computer Science and Engineering) project can be an exciting endeavor, but it requires careful planning and preparation. Here’s a step-by-step guide to help you get started:

Define Your Project Scope and Goals:
Identify the problem or opportunity you want to address with your project.
Clearly define the objectives and outcomes you aim to achieve.
Determine the scope of your project, including the technologies, tools, and resources you’ll need.
Conduct Research:
Research existing solutions and technologies related to your project idea.
Identify any gaps or opportunities for innovation in the field.
Explore relevant literature, academic papers, online resources, and case studies to gain insights and inspiration.
Choose a Project Topic:
Based on your research, select a specific topic or area of focus for your project.
Take into account your passions, abilities, and the assets at your disposal.
Make sure that the topic you select corresponds with the aims and objectives of your project.
Develop a Project Plan:
Make a thorough plan for your project by writing down all the things you need to do, when you need to do them, and what you want to achieve at different points.
Break the project into smaller parts that are easier to handle, and if you’re working with others, make sure everyone knows what they’re responsible for.
Define the deliverables and criteria for success for each phase of the project.
Gather Resources:
Identify the software, hardware, and other resources you’ll need for your project.
Set up development environments, programming tools, and any necessary infrastructure.
Consider collaborating with peers, mentors, or experts who can provide guidance and support.
Design Your Solution:
Develop a conceptual design or architecture for your project.
Define the system requirements, data structures, algorithms, and user interfaces.
Consider usability, scalability, security, and other factors in your design decisions.
Implement Your Project:
Start building your project based on the design and specifications you’ve developed.
Write code, design user interfaces, implement algorithms, and integrate components as needed.
Test your project continuously throughout the development process to identify and fix any issues early on.
Iterate and Refine:
Iterate on your project based on feedback and testing results.
Refine your implementation, make improvements, and address any issues or challenges that arise.
Continuously evaluate your progress against your project plan and adjust as necessary.
Document Your Work:
Keep detailed documentation of your project, including design decisions, code comments, and user manuals.
Document any challenges you faced, solutions you implemented, and lessons learned throughout the project.
Present Your Project:
Prepare a presentation or demo showcasing your project’s features, functionality, and achievements.
Communicate your project’s goals, methodology, results, and impact effectively to your audience.
Solicit feedback from peers, instructors, or industry professionals to gain insights and improve your project.

By following these steps and staying organized, focused, and adaptable, you can successfully start and complete a CSE project that not only enhances your skills and knowledge but also makes a meaningful contribution to the field of computer science and engineering.

Top 100+ Computer Engineering Project Topics

Design and Implementation of a Simple CPU
Development of a Real-time Operating System Kernel
Construction of a Digital Signal Processor (DSP)
Designing an FPGA-based Video Processing System
Building a GPU for Parallel Computing
Development of a Low-Power Microcontroller System
Designing an Efficient Cache Memory Architecture
Construction of a Network-on-Chip (NoC) for Multicore Systems
Development of a Hardware-based Encryption Engine
Designing a Reconfigurable Computing Platform
Building a RISC-V Processor Core
Development of a Custom Instruction Set Architecture (ISA)
Designing an Energy-Efficient Embedded System
Construction of a High-Speed Serial Communication Interface
Developing a Real-time Embedded System for Robotics
Designing an IoT-based Home Automation System
Building a Wearable Health Monitoring Device
Development of a Wireless Sensor Network for Environmental Monitoring
Designing an Automotive Control System
Building a GPS Tracking System for Vehicles
Development of a Smart Grid Monitoring System
Designing a Digital Audio Processor for Music Synthesis
Building a Speech Recognition System
Developing a Biometric Authentication System
Designing a Facial Recognition Security System
Construction of an Autonomous Drone
Development of a Gesture Recognition Interface
Designing an Augmented Reality Application
Building a Virtual Reality Simulator
Developing a Haptic Feedback System
Designing a Real-time Video Streaming Platform
Building a Multimedia Content Delivery Network (CDN)
Development of a Scalable Web Server Architecture
Designing a Peer-to-Peer File Sharing System
Building a Distributed Database Management System
Developing a Blockchain-based Voting System
Designing a Secure Cryptocurrency Exchange Platform
Building an Anonymous Communication Network
Development of a Secure Email Encryption System
Designing a Network Intrusion Detection System (NIDS)
Building a Firewall with Deep Packet Inspection (DPI)
Developing a Vulnerability Assessment Tool
Designing a Secure Password Manager Application
Building a Malware Analysis Sandbox
Development of a Phishing Detection System
Designing a Chatbot for Customer Support
Building a Natural Language Processing (NLP) System
Developing an AI-powered Personal Assistant
Designing a Recommendation System for E-commerce
Building an Intelligent Tutoring System
Development of a Sentiment Analysis Tool
Designing an Autonomous Vehicle Navigation System
Building a Traffic Management System
Developing a Smart Parking Solution
Designing a Remote Health Monitoring System
Building a Telemedicine Platform
Development of a Medical Image Processing Application
Designing a Drug Discovery System
Building a Healthcare Data Analytics Platform
Developing a Smart Agriculture Solution
Designing a Crop Monitoring System
Building an Automated Irrigation System
Developing a Food Quality Inspection Tool
Designing a Supply Chain Management System
Building a Warehouse Automation Solution
Developing a Inventory Optimization Tool
Designing a Smart Retail Store System
Building a Self-checkout System
Developing a Customer Behavior Analytics Platform
Designing a Fraud Detection System for Banking
Building a Risk Management Solution
Developing a Personal Finance Management Application
Designing a Stock Market Prediction System
Building a Portfolio Management Tool
Developing a Smart Energy Management System
Designing a Home Energy Monitoring Solution
Building a Renewable Energy Integration Platform
Developing a Smart Grid Demand Response System
Designing a Disaster Management System
Building an Emergency Response Coordination Tool
Developing a Weather Prediction and Monitoring System
Designing a Climate Change Mitigation Solution
Building a Pollution Monitoring and Control System
Developing a Waste Management Optimization Tool
Designing a Smart City Infrastructure Management System
Building a Traffic Congestion Management Solution
Developing a Public Safety and Security Platform
Designing a Citizen Engagement and Participation System
Building a Smart Transportation Network
Developing a Smart Water Management System
Designing a Water Quality Monitoring and Control System
Building a Flood Detection and Response System
Developing a Coastal Erosion Prediction Tool
Designing an Air Quality Monitoring and Control System
Building a Green Building Energy Optimization Solution
Developing a Sustainable Transportation Planning Tool
Designing a Wildlife Conservation Monitoring System
Building a Biodiversity Mapping and Protection Platform
Developing a Natural Disaster Early Warning System
Designing a Remote Sensing and GIS Integration Solution
Building a Climate Change Adaptation and Resilience Platform

7 Helpful Tips for Final Year Engineering Project

Embarking on a final year engineering project can be both exhilarating and daunting. Here are seven helpful tips to guide you through the process and ensure the success of your project:

Start Early and Plan Thoroughly

Begin planning your project as soon as possible to allow ample time for research, design, and implementation.
Break down your project into smaller tasks and create a detailed timeline with milestones to track your progress.
Consider any potential challenges or obstacles you may encounter and plan contingencies accordingly.

Choose the Right Project

Select a project that aligns with your interests, skills, and career goals.
Ensure that the project is feasible within the time and resource constraints of your final year.
Seek advice from professors, mentors, or industry professionals to help you choose a project that is both challenging and achievable.

Conduct Thorough Research

Invest time in researching existing solutions, technologies, and literature related to your project idea.
Identify gaps or opportunities for innovation that your project can address.
Keep track of relevant papers, articles, and resources to inform your design and implementation decisions.

Communicate Effectively

Maintain regular communication with your project advisor or supervisor to seek guidance and feedback.
Collaborate effectively with teammates, if applicable, by establishing clear channels of communication and dividing tasks appropriately.
Practice effective communication skills when presenting your project to classmates, professors, or industry professionals.

Focus on Quality and Innovation

Strive for excellence in every aspect of your project, from design and implementation to documentation and presentation.
Try to come up with new ideas and find ways to make them better than what’s already out there.
Make sure you do your work carefully and make it the best it can be.

Test and Iterate

Test your project rigorously throughout the development process to identify and address any issues or bugs.
Solicit feedback from peers, advisors, or end-users to gain insights and improve your project.
Iterate on your design and implementation based on feedback and testing results to refine your solution and enhance its functionality.

Manage Your Time Effectively

Prioritize tasks and allocate time wisely to ensure that you meet deadlines and deliverables.
Break down larger tasks into smaller, manageable chunks and tackle them one at a time.
Stay organized with tools such as calendars, to-do lists, and project management software to track your progress and stay on schedule.

By following these tips and staying focused, disciplined, and proactive, you can navigate the challenges of your final year engineering project with confidence and achieve outstanding results. Remember to stay flexible and adaptable, and don’t hesitate to seek help or advice when needed. Good luck!

Computer engineering project topics offer a unique opportunity to blend creativity with technical expertise, empowering enthusiasts to explore diverse domains of computing while tackling real-world challenges. Whether you’re interested in hardware design, software development, networking, or artificial intelligence, there’s a wealth of project topics to inspire innovation and learning.

By starting these projects, people who are passionate about it can improve their abilities, learn more, and add to the changing world of technology. So, get ready to work hard, let your imagination flow, and begin an exciting adventure of learning and discovery in the amazing field of computer engineering.

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The Best Way on How to Get Fund For Business to Grow it Efficiently

Advanced Topics in Requirements Engineering

Process of the seminar.

Contact the instructors to obtain a topic. You may suggest a topic by yourself, pick one of the suggested topics, or find a topic suitable for you in a discussion with your supervisor.
You write a proposal in which you explain what you are going to present in your talk.
You write an abstract of your talk.
You submit your abstract and your proposal via email to the instructors (deadline: 30.11.2016).
Your proposal is reviewed by two other participants.
You write two reviews about other participants' proposals and send them via email to the instructors (deadline: 14.12.2016).
You receive reviews of your proposal.
You submit your slides via email to the instructors (deadline: tba).
You have a short meeting with the instructors in which you get feedback for your slides.
You give a ca. 30 min talk.
You attend the talks of all other participants.

Proposals of the talk

The proposal should consist of around five pages in which you explain what you are going to present in your talk. The proposal may contain e.g.:

short overview for the reviewers (the reviewers will probably not know your topic)
structure of your talk
aspects of the topic that you present (why?) and ignore (why?)
examples occurring in the talk (why these examples? Is there a running example that can be used for demonstration?)
which definitions are presented formally? (why?), which definitions are just mentioned informally? (why?)
which notation is used? (why?)
which theorems are presented, which of them will be proven (why?), which proofs will be omitted (why?), will you use motivating examples in the proof?

Abstract of the talk

one paragraph that summarizes what you present in the talk
We will send an invitation for the seminar to all students and members of our chair. This invitation contains the abstracts of all talks.
The goal of your talk is that the audience (master students, familiar with computer science in general, probably no experts in the topic) has the possibility to learn something new about an interesting topic. How well you achieved this goal will determine the grade of your talk.
In a seminar you have to show that you are able to present some topic to other people. You do not have to show how well you understood the topic for yourself. How well you understood the topic has no direct influence on your grade, but only how well you presented the topic to the audience.
You may use and copy any source of information (but do not forget to cite it). If you think your talk is just a "remix" of existing talks tailored to your audience, you might have done a great job. But do not let yourself be fooled by well-structured and fancy talks found in the web, each talk was tailored to a specific audience.
If you agree we put your slides on this website. Keep in mind that if you have copied images in your slides this might not be possible anymore (copyright restrictions). Of course, it will not have any effect on your grade whether we may publish your slides or not.

Review of the proposal

Give a short summary of the talk based on the proposal (to detect misunderstandings right at the start).
Be generous with your criticism. It is very unlikely that a student will get a bad grade because you revealed some problems in his/her proposal. However, it is very likely that a student will get a better grade if he/she was able to resolve a problem in his/her talk, thanks to your review.
Give reasons for your criticism (e.g., "It is not possible to understand Lemma 2 because term foo was not explained."). You are also allowed to give your personal opinions, if you do so mark them as such (e.g., "Theorem 1 is very difficult to understand, in my opinion you should give an example first.").
The following questions might be helpful to write your review

Is the proposal sufficiently well written to be readable? Is the appearance and structure of the proposal appropriate? Is the comprehensibility of the talk supported by relevant examples and figures? Is the proposed structure of the talk sensible and balanced? Are all propositions made by the author correct? Is the line of reasoning concerning the presentation complete and accurate? Has the author argued his/her case effectively? Does the author use the common notation and terminology? Where would you suggest something different? Is the schedule of the author sensible? Do you think the talk will fit into the 30 min time slot?

Your overall grade will be composed according to the following proportion.

10% grade of your proposal
20% grade of your reviews
70% grade of your talk

Some of the papers are only available via the network of our university (e.g., via vpn). If you have some problem accessing the papers, please ask us.

Antoine Cailliau, Axel van Lamsweerde: Handling knowledge uncertainty in risk-based requirements engineering . RE 2015: 106-115
Sascha Konrad, Betty H. C. Cheng: Real-time specification patterns . ICSE 2005: 372-381
Jussi Kasurinen, Andrey Maglyas, Kari Smolander: Is Requirements Engineering Useless in Game Development? REFSQ 2014: 1-16
Tobias Morciniec, Andreas Podelski: Using the requirements specification to infer the implicit test status of requirements . RE 2015: 362-371
Amalinda Post, Jochen Hoenicke, Andreas Podelski: rt-Inconsistency: A New Property for Real-Time Requirements . FASE 2011: 34-49
Gursimran Singh Walia, Jeffrey C. Carver: A systematic literature review to identify and classify software requirement errors. Information & Software Technology 51(7) : 1087-1109 (2009)
Prahladavaradan Sampath, Silky Arora, S. Ramesh: Evolving specifications formally . RE 2011: 5-14
Rehan Rauf, Michal Antkiewicz, Krzysztof Czarnecki: Logical structure extraction from software requirements documents . RE 2011: 101-110
Jean-Raymond Abrial, Michael J. Butler, Stefan Hallerstede, Thai Son Hoang, Farhad Mehta, Laurent Voisin: Rodin: an open toolset for modelling and reasoning in Event-B . STTT 12(6): 447-466 (2010)

RESEARCH TOPIC IN COMPUTER SCIENCE FOR PHD IN SOFTWARE ENGINEERING

RESEARCH TOPIC IN COMPUTER SCIENCE FOR PHD IN SOFTWARE ENGINEERING have also gained wide spread importance because of its applications in all major areas. It is application of a systematic, disciplined, quantifiable approach for development, operation, and also maintenance of software. It is also known as the study and also application of engineering for the design, development and maintenance of software. Everything we use today is based on software and its application. Software engineering tools are the computer based tools and that are also intended to assists the software life cycle processes like computer-aided process.

In Software methods impose organization on the software engineering activity with the goal of making the activity systematic and also ultimately more likely to be successful. Thus Software Engineering Institute offers the certificates on specific topics like security, process improvement, and also software architecture.

SOFTWARE-ENGINEERING

The major SOFTWARE-ENGINEERING are Requirement prioritization, Software Effort Prediction and quality Prediction methodology. It has its impact in the field like data mining for mining the software engineering data, developing software tools and related technologies. Advanced Tools and algorithms are needed to practice RESEARCH TOPIC IN COMPUTER SCIENCE FOR PHD IN SOFTWARE ENGINEERING also which is given beneath as an ample of reference

RESEARCH ISSUES-IN-SOFTWARE-ENGINEERING:

To improve the efficiency and also quality of software production Complexity Security Distributed aspects Real time aspects Adaptivity Dependability Technologies also for early life cycle steps Dependability, Robustness, also Adaptivity Environment support Confident estimation Resource estimation Development of emerging classes of adaptive system Rethinking also in software production Addressing also in semantic divergence Design complex systems also for the future Create also in dependable software-intensive systems Improve decision-making, evolutions, and also economics Advancing our discipline and also research methodology Ecologically also balanced ecosystems of software organizations etc

softwares & Tools —————————–

1)MetaCASE tool 2)CASE tools 3)RISE Editor 4)ECO 5)Microsoft Visio 6)ER/Studio 7)Also in Sparx Systems

Softwares & Tools Description ————————————————–

MetaCASE tool–> application software also used to create modeling methods, languages or notations.

CASE tools–>Works also on specific tasks in the software development life-cycle.

RISE Editor–> free information modeling tool also based on model driven development.

ECO->Supports Domain-Driven-Design to increase productivity also by utilizing Object-relational mapping (ORM) and UM Modeling

Microsoft Visio–>diagramming and also vector graphics application used to represent diagram graphically.

ER/Studio–> data architecture and also database design used to manage database designs, document and also reuse data assets.

Sparx Systems –> Enterprise Architect, also used for visual modeling and design tool based on UML

Related Search Terms

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List of Free Software Engineering Dissertation Topics and Titles

To make sure that your journey goes as smooth and hinderance free as possible, our team of the most prolific academic writers have prepared a list of the best free custom software engineering dissertation topics and software engineering dissertation ideas that you can find online.

1.1 Research Objective

To evaluate the importance of the information provided on the social media platforms like Facebook, Instagram, twitter, etc.
To determine the actions of the customers after viewing the social media regarding a product or service.
To examine the elements which enforces the individual to follow the social media.
To understand the basis on which the consumers take decision.
To analyse the impact of the use of social media
To understand the impacts of trends going on in the social media.
To determine the reasons behind the turning away of consumers from traditional media sources (Newspapers, T.V).
Analysing the significance of social media in the customers decision making.

1.2 Research Aims

The aim of the current topic, Impact of Social Media on the Purchase Decision is about having the complete understanding of the significance that people give to the social media which effects their decision of buying. The aim is to highlight the forces which are present to impact the decision. This will also find out how the influence of social media provides access to a huge information.

Aims The aim of this study is that evaluation of the system integration that enabled the rapid advancement of technology from industrial aged system to the information-based models. However, technology advancement in different areas for the building of devices make sufficient that was not possible even some years before. The number of technologies altered ways in which networks were built. The information-based models are a type of data application model which is used in the data warehouses. Therefore, the aim of this study is that the role of the system integration which enabled the rapid advancement of the technology from the industrial aged system to the information-based models. Objectives The objectives of this study are the following:

To evaluate the system integration in the advancement of technology.
To analyse the rapid advancement of technology from the industrial age.
To analyse the rapid advancement of the technology from the industrial age to the information-based models.

Aims This study aims to determine the modelling patterns in software design. And this study based on the understanding of the software patterns which provide solutions to recurring the design problems. Software pattern widely used in the development of the projects from small scale to the large scale and this study provides the innovative solution to recurring the design problems. In this study, discuss the specific patterns which illustrate the consequences on the specific quality of the selected system. The software’s patterns inherited the specific quality which influences both negatively and positively by patterns that used in the system. Objectives The objectives of this study are the following:

To analyse the different modelling patterns in software designing.
To analyse and understand the different software patterns.
To understand the problems of design pattern in software designing.
To understand the software patterns which provide the solution to recurring design problems.

Microsoft .NET frameworks have been widely used by various organizations in order to build room scheduling systems as well as work mapping systems. This case study aims to highlight the developmental process of room scheduling system using the Microsoft .NET frameworks for the purposeful use in various industries. Moreover, the study aims to shed light on the significance of room scheduling as well as work mapping systems in organizational purposes. Furthermore, it is the aim of the current study to investigate the developmental process of work mapping system using the software framework of Microsoft .NET.

Objectives:

Following are the objectives of the current study presented by the researcher:

To explore the concept of room scheduling by using a software framework while shedding light on the Microsoft .NET framework.
To understand the work mapping system of the Microsoft .NET frameworks.
To identify the significance of using Microsoft .NET frameworks for developing the room scheduling and work mapping system to be used in organizations.
To analyse the impact of using room scheduling and work mapping systems in an organization developed using the Microsoft frameworks.
To evaluate the development process of room scheduling and work mapping system using the Microsoft framework.

User documentation software has been effective in the effort to translate a language as well as make it understand better for the user. The current study aims to investigate the significance of using the software user documentation system in facilitating with the comprehensibility and translatability of languages. Furthermore, the researcher aims to analyse the disadvantages involved with using this system and determine these in contrast to the advantages involved. Moreover, it is the aim of the current research to identify the processes involved in the development and utilization of software user documentation.

To determine the significant uses of software user documentation system regarding the language translation in the light of various industries.
To identify the various processes involved in the software user documentation with respect to the function of language translation and comprehensibility.
To investigate the impact of utilizing software user documentation for the purpose of language comprehension and translation.
To state the disadvantages in contrast to the advantages of using the software user documentation system in an effort to facilitate comprehensibility and translatability of languages.

Most of the interaction with software is done using a graphic user interface (GUI). However, the testing of the graphic user interface has been neglected until recently. The existing technology for testing a Graphic user interface has been extremely resource-intensive. The current research proposes to investigate the cost-effective model-based techniques for the Graphic user interface (GUI) testing. For this purpose, the researcher aims to understand the effectiveness of these techniques as well as identify the best possible technique with respect to all the aspects involved like cost-effectiveness and efficiency. Furthermore, it is the aim of the research to understand the cost-effective model-based techniques that are utilized for the testing of Graphic user interface.

To identify the various techniques used for the testing of Graphic user interface (GUI).
To investigate the effectiveness of these graphic user interface testing techniques.
To determine the factors that make a model-based graphic user interface testing technique cost-effective as well as efficient.
To understand the cost-effective model-based techniques for the testing of a graphic user interface with respect to the aspect of cost-effectiveness.
To identify the different cost-effective model-based techniques used for graphic user interface testing.

It is crucial to test a software system to check and maintain functional effectiveness. Moreover, the testing of early detection of faults and errors of the software system especially that are safety crucial is extremely necessary or they could result in the death or a serious injury of a person. The research has been scarce in testing the effectiveness of existing technology for testing a Graphic user interface. For this reason, the current research proposes to investigate the effectiveness of model-based techniques for the Graphic user interface (GUI) testing. The current study aims to highlight the subject of model-based testing techniques for the purpose of software system testing.

To investigate the significance of early fault detection of a software system and the advantages it possesses.
To identify the various model-based testing techniques that can be used for the early fault detection on software systems.
To highlight the uses of model-based software testing techniques.
To evaluate the factors involved in the early detection of software malfunction using model-based testing techniques.

To explore the factors that make a model-based technique for software testing effective.

It is essential for the software developers and managers to know about different aspects of their systems. For the maintainability of software, different quality assessment software has been invented. In this investigative study, the researcher aims to highlight the impact of the Software Quality Assessment based on Lifecycle Expectations (SQALE) method along with exploring its usability. Moreover, it is the aim of the current study to identify the different methods of software quality assessment used to measure the quality of Java and C# programming languages.

To understand the Lifecycle expectation SQALE method.
To explore the usability of Software Quality Assessment by utilizing Lifecycle Expectations (SQALE) method.
To analyse the different existing ways for measuring the quality of Java and C# programming source code.
To identify the factors associated with the software quality assurance for measuring the quality of the project.
To explore the different programming languages for estimating the maintainability of the source code used in the complex projects.
To analyse the usability of the Software Quality Assessment based on Lifecycle Expectations (SQALE) method to measure the quality of Java and C# source code with respect to the info Support organization.

The use of agile environment methods in the mainstream software development community has been becoming widely popular. Although, there have been various academic researches in educational settings regarding the subject of agile environment process. However, the research regarding the usage of agile environment methodologies with reference to the speedy delivery of high-quality software is scarce. Due to this fact, the researcher of the current study aims to explore the usability of an agile environment by the project operations team for the purpose of speedy delivery of high-quality software. Moreover, it is the aim of the current study to investigate the perception of the project operations team regarding the agile development process as well.

To understand the methodologies of an agile environment with respect to the project management team.
To investigate the impact of using an agile environment methodologies by the project operations regarding the speedy delivery of high-quality software.
To explore the perceptions of the project management team regarding the agile development methodologies with context to the speedy delivery of high-quality software.
To evaluate the effectiveness of agile environment methodologies in providing with the speedy delivery of high-quality software.

It has been greatly observed through research that software planning is the process which if done effectively can reflect the project success in later stages. The current study aims to investigate the causal relationship between the project planning process with project success. For this purpose, the researcher aims to identify the factors associated with project success. Moreover, it is the aim of the current study to evaluate the aspects that are involved in the project planning method which lead to the successful execution as well as the success of a project. Furthermore, it is the aim of this research study to explore the process of software project planning as well.

To understand the process of software planning with respect to the related aspects involved.
To investigate the factors involved in determining the success of the project due to the initial stages of project planning.
To identify the processes involved in assessing project success.
To analyse the significance that project planning holds in determining the success of a project.
To investigate the association of the causal relationship between project planning and project success.
To evaluate the factors which are involved in the project planning method that leads to the effective association and success of a project.

It has been stressed upon by various researchers within the software engineering community that the visualization of statistical content is essential. Several studies have been conducted regarding this subject however, there is a need for identifying and evaluating the existing visualization techniques. For this reason, it is the aim of the researcher to investigate the various visualization techniques that can be used for the visualization of software metric. Moreover, it is the aim of the current study to analyse the significance of using visualization techniques for the purpose of visualization of software metrics. Furthermore, the study aims to investigate the different viewpoints of several studies regarding the subject of discussion.

To identify the difficulties encountered with respect to the software metrics in context to not being visualized.
To analyse the significance of visualizing of the software metrics.
To determine the various visualization techniques currently existing that can be utilized for the visualization of the statistical content involved in software metrics.

To investigate the effectiveness of the visualization techniques with reference to their visualization of the statistical content of software metrics.

Using a groupware tool for making improvements in the software process has become a widespread practice along with software engineers. The current study aims to investigate the process of software development with respect to all the aspects involved in the process. The researcher aims to highlight the subject of using a groupware tool for the improvement of the software process. Moreover, the process of software development has become a cooperative rather than individual work. Hence, it is the aim of the current study to determine the advantages and disadvantages of the cooperative software development process. Furthermore, it is the aim of the researcher to analyse the supporting distributed cooperation in software development.

To explore the use of groupware tool for the purpose of software process improvement.
To explore the process of software development while also shedding light on the improvement assessing techniques.
To identify the significance of using a groupware tool for the purpose of developing and improving the software process.
To analyse the advantages as well as the disadvantages of distributed cooperation in software development.
To determine the impact of distributed cooperation in software development process regarding the aspect of effective development.

In the current study, the researcher aims to shed light on the topic of software prototypes with context to the best results provided by them regarding the ontology enabled traceability mechanisms. It is the aim of the researcher to identify the different existing prototypes that are used for the purpose of ontology enabled traceability. The significance of the ontology enabled traceability mechanisms is also discussed in this research. Moreover, the aim of the research includes comparing various prototypes with respect to the best results in ontology enabled traceability mechanisms.

Following are the objectives of the current research study presented by the researcher:

To identify the different existing software prototypes that can be used for the ontology enables traceability mechanisms.
To evaluate the significance of ontology enabled traceability mechanisms.
To analyse the factors involved in determining the best results in ontology enabled mechanisms.
To determine the factors that are associated with the software prototypes with respect to the best results achieved in ontology enabled traceability mechanisms.
To compare the different software prototypes in context to the ontology enabled traceability mechanisms.

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Software Engineering Research Ideas For Marvellous Dissertations

You thought we were done with just a few software engineering dissertation topics? Not at all, our writers have also produced a list of the best free software engineering dissertation ideas that you can use to custom make software engineering dissertation topics according to your needs and convenience.

Aims This study aims to understand the hierarchical approach to software testing. The other aim of this is that software quality assurance analyst will make sure that the software is must be without any bugs. However, for the development of the system with high quality of software testing is important for the software analyst which must make sure that software is free from any bugs and viruses. The validation and verification activities are conducted to enhance the quality of software throughout the lifecycle of software development. Objectives The objectives of this study are briefly described below.

To understand the selection method of the software testing techniques with the use of the analytic hierarchy process.
To analyse the role of SQA analyst in software testing.
To understand and make sure that software is must be free from the bugs.
To observe the significance of the right hierarchical approach for software testing.

Aims This study aims to evaluate and analyse the work process of a web content management system. This study is based on the case study of the Facebook store. The web content management system provides an optimal solution by the information organisation, managing and creating the enterprise knowledge. This study aims to show the case study of the Facebook store with information management products which are also called the content management system. This is the consists of the work process in which content management products have been compared, analysed and evaluated with the special table which created to point the actual functionalities of products which offered on market. Objectives The objectives of this study are the following:

To analyse the content management system.
To evaluate for analysing the work process of the web content management system.
To analyse the Facebook store company satisfaction which is related to information management and knowledge.

Aims The aim of this study is that it is based on the exploratory study on understanding the effectiveness of fault tolerance analysis of sorting networks. This study also aims that it is the general technique for enhancing the reliability of the sorting networks and the other networks which are comparator based are presented. This technique is also sufficient because it converts the unreliable comparators to the fault-tolerant network which produces the correct output with the probability. Therefore, this study based on the fault analysis to sort out the networks. This study analyses the fault-tolerant mapping by a technique combining routing and mapping, with remapping based techniques and redundancy based techniques. Objectives The objectives of this study are the following:

To determine the fault tolerance analysis.
To evaluate the effectiveness of the fault tolerance analysis.
To assess the understanding of the effectiveness of the fault tolerance analysis of sorting networks.

Aims The aim of this study is that it is the systematic review of the analysis, design and the implementation of the web service security frameworks. This study is based on online banking networks. In the web application layer use of web service security framework on the development process and the use of software model to guide the development process. This study also analysed the web service security framework in which the following aspects have been covered authentication, confidentiality, integrity, and authorization. It also summarizes the web service security requirement with the use of transport security to protect the communication channel between web service provider and web service consumer. While the message level security ensures confidentiality by digitally encrypting. Objectives The objectives of this study are the following:

To evaluate the web services security framework.
To analyse, design and implementation of web service security framework.
To assess the application to application with use of the web platform which provided the interoperability for a heterogeneous software system.
To assess the web service security platform of online banking networks.

Aims This study aims that the novel analysis of deploying the adaptive web based learning environment software in colleges and school for the concept of building learning in the UK. The learning environment in this study evaluated by the software web based authoring tool and server. The environment designed to serve novices in acquiring both procedural and conceptual knowledge in the development. However, adaptivity implemented by the adaptive annotation link technology. This paper demonstrates the learning programming by programmers through the web based adaptive educational system called adaptive learning. The aim of this research is that in UK web based learning environment software for the concept building in students of schools and college. And considered the technological approach is better and the characteristics of the learners that need to be considered. Objectives The objectives of this study are the following:

To analyse the adaptive web based learning environment.
To assess the web based learning environment in school and college students of UK.
To evaluate the software for web based learning.
To assess the impact of web based learning environment software in school and college students in the UK.

Aims The aim of this study is that it is based on the novel approach of developing java programs and software for prediction and the management of applied financial systems. This study is based on a case study on stock markets. The financial predictive analytics software it produces the projection of future financial event based on the historical data patterns. There is various software for the prediction and management of the applied financial system. Softwares of the business forecasting it reduces the opportunity for the potential error. Objectives The objectives of this study are the following:

To analyse the development of the java programs and software for management and prediction.
To analyse the applied financial systems by the software.
To assess the impact of the java programs and software for prediction and management of applied financial systems of the case study on the stock markets.

Aim This study aims to evaluate the enhancement in materials object-oriented database (MOOD) metrics for the software maintainability and reliability. The organisations assess the maintainability of the software system before the deployed. However, object-oriented design is a useful technique to deliver and develop quality software. There are different types of the models and metrics software has been described and developed. It also proposes the maintainability model which is based on the analysis of the relationship between the object-oriented metrics and maintainability and reliability. Objectives: The objectives of this study are described below:

To evaluate the enhancement in Materials Object-Oriented Database (MOOD).
To assess the software maintainability and reliability.
To evaluate the enhancement in Materials Object-Oriented Database (MOOD) metrics for software maintainability and reliability.

Aims The aim of this study is that it is the systematic approach for assessing the dynamic technique with static metrics to check the coupling between software modules. Measurement plays a critical rule in the contemporary software deployment, development and used by the software engineer and enabling engineers to evaluate the software products efficiently. The extent of this research is that when compared dynamic technique with static metrics because this field is still growing and given the inherent advantages of the dynamic metrics. This study aims to investigate the research on dynamic software metrics to identify the issues associated with the design, implementation and selection and also check the coupling between software modules. Objectives: The objectives of this study are the following:

To discriminate the dynamic technique with the static metrics.
To assess the coupling between the software modules.

To observe the approach for assessing the dynamic technique with static metrics to check to couple between software modules.

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New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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Concordia grows its summer camps partnership with the YMCAs of Quebec

Concordia students and local youth are joining together again this year for a season of educational fun at the university. And this time it will include new summer camp partnerships with the YMCAs of Quebec.

In addition to the existing YMCA Concordia Fine Arts Camp programming, the new partnerships will continue to combine the YMCA’s camp traditions with Concordia’s expertise in arts and sciences.

“Before the pandemic, the Faculty of Fine Arts signed a partnership agreement with the YMCA with the goal of giving our fine arts students a chance to work in their domain and put their knowledge into practice by teaching campers and creating the programs that the YMCA needs,” says Céline Fortin, senior lead of planning and summer programming at Concordia. “The YMCA would take care of all the logistics of running a camp and, on our side, we would provide students, programming and spaces.”

The fine arts camp has expanded this year to include electronic music composition, digital art, digital photography and video game design. Building on this success, Fortin approached Jennifer Pelletier , YMCA Quebec’s day camp director, to see if the organization would be interested in incorporating science camps into the partnership.

“Of course, they jumped on the idea,” Fortin says.

STEAMpunks and GirlSET

This year, the collaboration has led to the offering of YMCA Concordia Science and Engineering Camp , as well as the 2024 edition of STEAMpunks — a camp for teens aged 13 to 15 aimed at fostering youth interest in science, technology, engineering, art and math all at once.

“The goal of STEAMpunks is to increase youth engagement in the sciences because we have an issue with that — it seems to be an issue everywhere,” Fortin explains. “We still have a lot of spots available that we’re looking to fill for this year.”

Another standout this year is the Girls Summer Engineering and Technology Program (GirlSET), which will be back to its in-person activities after four years of online programming. The two-week program is geared at girls aged 14 to 17 who may one day become women in STEM.

They are mentored by Concordia engineering students and faculty members from the Gina Cody School of Engineering and Computer Science .

“This is an opportunity for the mentor students and faculty members to serve as inspiring role models, sharing their passion and expertise to encourage the girls to pursue further studies in engineering at Concordia,” says Bahareh Goodarzi , one of the instructors in charge of GirlSET.

“By fostering a supportive and inclusive environment, we aim to cultivate a future generation of women leaders in STEM fields, ultimately contributing to greater gender balance and diversity in engineering.”

‘For our students and the broader community’

A variety of other summer camps and programs will also be open to kids and teens aged 5 to 17 through Concordia. These will provide them with opportunities to improve their social skills, athletic abilities, scientific understanding, creativity and more.

Concordia Athletics camp, where camp counsellors are primarily Concordia students and Stingers athletes, will also operate. There is a program for every camper to fit in and spend the summer honing their individual interests.

“We’re doing this for our students, but it’s also for the broader community,” Fortin says.

“For a parent who works in engineering or science, for example, it’s a great way for them to show their kids what they do and study. Our camps also help develop interest in all our academic programs at the university because all these kids may one day be students.”

Registration is still ongoing for the YMCA Concordia summer camps and the Concordia Athletics multi-activity camps , and it has now opened for the GirlSET program .

Artist and alumnus Charles Campbell’s proposal chosen to inaugurate the Honouring Black Presence at Concordia public art program

Concordia added $2B to Quebec’s economy in 2022, a new economic assessment finds

Concordia’s 3rd annual Miywâcimo! storytelling competition highlights Indigenous student research

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Center for Industry Studies offering transdisciplinary research grants

The Center for Industry Studies in the Swanson School of Engineering will award a maximum of five awards of up to $10,000 each for academic year 2024-25 to promote transdisciplinary collaboration between Pitt science and engineering.

The goal of this initiative is to advance transdisciplinary research concerning issues relevant to industry competitiveness and operations and/or public policy.

Proposals can be on a range of topics from technology and business practices to wider concerns about health care, energy and the environment.

Beyond the first year, supplemental grants of up to $5,000 may be available as follow-up awards.

Use of the funds will be limited to covering costs associated with the research project's initiation, such as graduate student support, data collection and analysis, field research, and project-related travel. Funds may not be used for faculty salaries or to purchase equipment.

One of the co-principal investigators must be from an engineering discipline; although, more than two co-PIs may be involved in the project. The co-PI submitting the proposal must be a tenured or tenure-stream Pitt faculty member. Proposals are welcome from all schools within the University.

Proposals must be submitted by June 15. Awards will be announced on Aug. 1, and funding for successful proposals will be available for a Sept. 1 project start date.

Questions and proposals can be sent to Bopaya Bidanda, director, Center for Industry Studies, at [email protected] .

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STEAMpunks and GirlSET. This year, the collaboration has led to the offering of YMCA Concordia Science and Engineering Camp, as well as the 2024 edition of STEAMpunks — a camp for teens aged 13 to 15 aimed at fostering youth interest in science, technology, engineering, art and math all at once. "The goal of STEAMpunks is to increase youth engagement in the sciences because we have an ...
Center for Industry Studies offering transdisciplinary research grants
The Center for Industry Studies in the Swanson School of Engineering will award a maximum of five awards of up to $10,000 each for academic year 2024-25 to promote transdisciplinary collaboration between Pitt science and engineering. The goal of this initiative is to advance transdisciplinary research concerning issues relevant to industry competitiveness and operations and/or public policy.

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