7 papers · 1 filter
Empirical Study on the Characteristics and Evolution of AI-usage in GitHub Repositories: Evidence from Code Comments
Abdullah Al Mujahid, Preetha Chatterjee, Mia Mohammad Imran
Developers increasingly use AI tools such as ChatGPT, Copilot, and Claude in everyday software workflows, but prior studies often evaluate LLM outputs in isolation rather than exam…
Improving Code Comprehension through Cognitive-Load Aware Automated Refactoring for Novice Programmers
Subarna Saha, Alif Al Hasan, Fariha Tanjim Shifat +1
Novice programmers often struggle to comprehend code due to vague naming, deep nesting, and poor structural organization. While explanations may offer partial support, they typical…
DePro: Understanding the Role of LLMs in Debugging Competitive Programming Code
Nabiha Parvez, Tanvin Sarkar Pallab, Mia Mohammad Imran +1
Debugging consumes a substantial portion of the software development lifecycle, yet the effectiveness of Large Language Models(LLMs) in this task is not well understood. Competitiv…
OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering
Mia Mohammad Imran, Tarannum Shaila Zaman
Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative…
"TODO: Fix the Mess Gemini Created": Towards Understanding GenAI-Induced Self-Admitted Technical Debt
Abdullah Al Mujahid, Mia Mohammad Imran
As large language models (LLMs) such as ChatGPT, Copilot, Claude, and Gemini become integrated into software development workflows, developers increasingly leave traces of AI invol…
LLPut: Investigating Large Language Models for Bug Report-Based Input Generation
Alif Al Hasan, Subarna Saha, Mia Mohammad Imran +1
Failure-inducing inputs play a crucial role in diagnosing and analyzing software bugs. Bug reports typically contain these inputs, which developers extract to facilitate debugging.…