Joy Krishan Das

Joy Krishan Das

In a world where LLMs can do many things humans once did themselves, the question of ‘how will I add value?’ becomes more relevant than ever.

I am a master’s student in computer science at the University of Saskatchewan. I conduct research at the Software Research Lab under the supervision of Prof. Chanchal Roy, focusing on automated program repair, software mining, and the application of large language models in software engineering. In my free time, I enjoy football, reading books, and photography.

Research Interest

My research interests lie in Software Engineering, particularly in automated techniques for resolving software issues with minimal human intervention. Debugging is a time-consuming and costly process, consuming a substantial portion of development efforts. With the advent of SWE-bench, automated issue resolution using large language models holds significant promise. However, existing methods struggle with small code segments and rely on inflexible feedback loops, unlike the adaptive, iterative debugging approach of human developers. My work explores agent-based techniques that empower LLMs to autonomously resolve issues at the project level, reducing developer effort and enabling a focus on more creative tasks.

Publications

  • Joy Krishan Das, Saikat Mondal, and Chanchal K. Roy, "Why Do Developers Engage with ChatGPT in Issue-Tracker? Investigating Usage and Reliance on ChatGPT-Generated Code". 32nd International Conference on Software Analysis, Evolution, and Reengineering (SANER 2025) (Accepted)
  • Joy Krishan Das, Saikat Mondal, and Chanchal K. Roy, "Investigating the Utility of ChatGPT in the Issue Tracking System: An Exploratory Study," 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR 2024), Lisbon, Portugal
  • ST Cynthia, Saikat Mondal, Joy Krishan Das, and Banani Roy, "Gender Disparities in Contributions, Leadership, and Collaboration: An Exploratory Study on Software Systems Research". Fifth Workshop on Gender Equality, Diversity, and Inclusion in Software Engineering (GE@ICSE 2025) (Accepted)
  • Joy Krishan Das, Md. Jalil Piran, and Amitabha Chakrabarty. "Environmental sound classification using convolution neural networks with different integrated loss functions." Expert Systems 39, no. 5 (2021): e12804
  • Joy Krishan Das, Arka Ghosh, Abhijit Pal, Sumit Dutta, and Amitabha Chakrabarty. "Urban Sound Classification Using Convolutional Neural Network and Long Short Term Memory Based on Multiple Features." in Proceedings of the Fourth International Conference On Intelligent Computing in Data Sciences (ICDS 2020). IEEE, 2020

Education

  • 🎓 Master of Computer Science (MSc), University of Saskatchewan (2023-Present)
         Grade: 91%
  • 🎓 Bachelor of Science in Computer Science, BRAC University (2016-2020)
         CGPA: 3.92/4.00, Summa Cum Laude (19th Convocation)

Experience

  • 📚 Graduate Teaching Assistant
    University of Saskatchewan, Saskatoon, Canada (Sep 2023 -- Present)
    Graded assignments for Introduction to Computer Science and Programming Language Paradigms. Provided tutorials for Website Design and Development.

  • 💻 Software Engineer, QA
    Therap (BD) Ltd., Dhaka, Bangladesh (Sep 2021 -- Jul 2023)
    Implemented automated QA processes with Java, Selenium WebDriver, and REST Assured. Conducted various types of testing and shared domain knowledge.

  • 📚 Lecturer (On Contract)
    BRAC University, Dhaka, Bangladesh (Oct 2020 -- Jan 2021)
    Created lesson plans for Artificial Intelligence and Introduction to Computer Science. Offered support during office hours.

  • 📚 Undergraduate Teaching Assistant
    BRAC University, Dhaka, Bangladesh (Jan 2019 -- Apr 2020)
    Assisted with Java in Programming Language I and II courses. Helped students with MATLAB in Numerical Methods.

Volunteer Work

  • 🤝 Vice President Internal
    Computer Science Graduate Council (2023 -- 2024)
    As Vice President Internal at USask, the user oversaw website management, documented meeting minutes, and coordinated Research Fest 2024.
  • 👨‍🏫 Mentor
    Deep Learning.AI (Feb 2021 -- Jul 2021)
    Supported learners in Natural Language Processing with Classification and Vector Spaces on the coding assignments of Sentiment Analysis, Naive Bayes, Vector Space Models, and Word Translation.
  • 📚 Section Leader
    Code In Place, Stanford University (Apr 2021 -- Jun 2021)
    Selected as one of 908 volunteer section leaders to teach weekly interactive learning sessions of Stanford's CS1.
  • 👨‍🎓 FYAT Mentor
    BRAC University (Apr 2018 -- Jan 2019)
    Advised first year students with course planning and selection.