10 papers
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…
Towards SocratiCode: Designing a Generative AI-Based Programming Tutor for K-12 Students through a 4-Week Participatory Design Study
Cassandra Lucas, Anshul Bihani, Rohini Kukka +3
Generative AI creates new opportunities for programming education, but many existing systems remain overly directive, producing lengthy explanations and premature solutions that ca…
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…
LLM-Enabled Open-Source Systems in the Wild: An Empirical Study of Vulnerabilities in GitHub Security Advisories
Fariha Tanjim Shifat, Hariswar Baburaj, Ce Zhou +2
Large language models (LLMs) are increasingly embedded in open-source software (OSS) ecosystems, creating complex interactions among natural language prompts, probabilistic model o…
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…
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.…