Hi @Noor Ul Ain Ansari

I suggest you choose your topic in the following areas:

1. Modeling the selection of the appropriate process when an event fits the problem using an expert decision-making system based on fuzzy multi-criteria decision making

2. Designing an integrated system with the fuzzy IoT theory

Good luck

Hi Noor Ul Ain Ansari

I suggest one of the following 2 topics:

1. Inclusion of wearable devices on software engineering systems.

2. Software engineering integrated in Knowledge systems. I'm referring to human centered knowledge systems were software applications are seem as a components of the knowledge system, not as an isolated product.

Good luck.

There are the few suggestions.

1. Defect prediction in Android systems.

2. Bug localization in web-based applications

3. Effective test case generation

4. Prioritization of test cases

5. Test case minimization.

1. Why is it that the more you think you are a good software engineer, the less you are?

2. In 1995 the Chaos studies by Standish indicated high failure rates for software projects; why, 25 years later are failures still high?

What is your general interest area?

Currently e.g. AI and sustainability are highly popular topics:

Exploring Industry 4.0 Technologies to Enhance Circularity in Textile Industry: Role of Internet of Things

https://www.researchgate.net/publication/339592939_Exploring_Industry_40_Technologies_to_Enhance_Circularity_in_Textile_Industry_Role_of_Internet_of_Things

New Promises AI Brings Into Circular Economy Accelerated Product Design: Review on Supporting Literature

https://www.researchgate.net/publication/336276030_New_Promises_AI_Brings_Into_Circular_Economy_Accelerated_Product_Design_Review_on_Supporting_Literature

Key Enablers for Deploying Artificial Intelligence for Circular Economy Embracing Sustainable Product Design: Three Case Studies

https://www.researchgate.net/publication/337170902_Key_Enablers_for_Deploying_Artificial_Intelligence_for_Circular_Economy_Embracing_Sustainable_Product_Design_Three_Case_Studies

But it does not mean you should be looking what is popular... You should be considering where you want to contribute into.

Why don’t you look into this: https://www.researchgate.net/post/Why_Every_Revolutionary_Discovery_Truths_are_treated_as_Heresy_Blasphemies

This is very simple and primitive. All you need to understand is what is “Component-based engineering”: http://real-software-components.com/raju/TwoKindsOfParadigms.pdf

Of course, this challenges the basic primitive flawed presumptions. So, you have to be maverick to overcome flat-earth people, pretending to be scientists promoting the lie that reusable-parts are components and CBD is using such fake components.

You can’t find a topic simpler than this: http://real-software-components.com/raju/ImportantFacts.pdf

Plus side is that you are contributing to a paradigm shift by subverting existing dominant paradigm and changing the world.

you could also take a look to research gaps, other people have politely pointed out, ready to be considered :-)

Software Engineering Research Gaps in the Cloud

https://econpapers.repec.org/article/iggjitr00/v_3a6_3ay_3a2013_3ai_3a1_3ap_3a1-19.htm

Here are the latest research topics related to SE:

• Fault detection in software using biological techniques
• Enhancement in MOOD metrics for software maintainability and reliability
• To enhance effort estimation using Function point analysis in Cocomo model
• To evaluate and improve model based mutation technique to detect test cases error in product line testing
• To propose improvement in genetic algorithm to calculate function dependency in test case prioritization in regression testing
• To propose dynamic technique with static metrics to check coupling between software modules
• To propose improvement TYPE 4 clone detection in clone testing

You definitely want to take a look into here:

Understanding the Knowledge Gaps of Software Engineers: An Empirical Analysis Based on SWEBOK

Article Understanding the knowledge gaps of software engineers: An e...

B.R.,

Ari

I would recommend that you work on a topic related to "Mining Software Repositories (MSR)". In this topic, people employ statistical/data mining/machine learning techniques with software engineering.

In short, the area explores historical data of software development to:

• discover problems and causes related to software development
• perform modeling/prediction of real software development practices
• generate insights to software developers/engineers/practitioners on how to overcome the challenges of software development

There are many grounds in this area where you can land your master's thesis. There are also numerous publically available datasets that you can acquire and start looking for a specific problem to study. You may check the following conference to see what researchers are currently working on recently:

https://2020.msrconf.org/program/program-msr-2020

The nice thing about this area is that you can conduct your research on any topic (including those proposed in the other answers) across the history of software rather than a single software snapshot. Typically, research of this kind is referred to as "Empirical Software Engineering". You may consult the following top-tier journal to see recent empirical software engineering studies:

https://link.springer.com/journal/10664/volumes-and-issues

Hope this helps you.

Best wishes,

Taher

Hi,

I think you could also look short term future predictions:

5 Trends Drive the Gartner Hype Cycle for Emerging Technologies, 2020

https://www.gartner.com/smarterwithgartner/5-trends-drive-the-gartner-hype-cycle-for-emerging-technologies-2020/

Various topics are the current focus of research in software engineering.

Some of them are as follows:

1. Automatic program repair

2. Effective fault localization

3. Improvement in available software metrics for integration testing and system testing.

4. Agile software development

5. Model-Driven Development

6. Software Evolution and Maintenance

7. Mining Software Repositories

8. System Verification and Validation

9. Reusable and Extendable AI models

10. ML Lifecycle management

11. ML model Catalogs and Searching for Rapid Development

12. Testing, Correctness, Debugging & Interpretability of AI/ML applications

Have you considered to speak with the area specific professors in your University?

B.R.,

Ari

Definitely,

and more specifically e.g. why applying of AI is a challenge, where Neural Networks and ML solutions could fail, even when well planned and other similar bit more specific topics.