5 papers
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…
Learning Programming in Informal Spaces: Using Emotion as a Lens to Understand Novice Struggles on r/learnprogramming
Alif Al Hasan, Subarna Saha, Mia Mohammad Imran
Novice programmers experience emotional difficulties in informal online learning environments, where confusion and frustration can hinder motivation and learning outcomes. This stu…
"Silent Is Not Actually Silent": An Investigation of Toxicity on Bug Report Discussion
Mia Mohammad Imran, Jaydeb Sarker
Toxicity in bug report discussions poses significant challenges to the collaborative dynamics of open-source software development. Bug reports are crucial for identifying and resol…
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.…