7 citations · 25 across the 29 of their papers we have counts for
10 papers · 1 filter
Toxicity Ahead: Forecasting Conversational Derailment on GitHub
Mia Mohammad Imran, Robert Zita, Rahat Rizvi Rahman +2
Toxic interactions in Open Source Software (OSS) communities reduce contributor engagement and threaten project sustainability. Preventing such toxicity before it emerges requires…
AI-Powered Commit Explorer (APCE)
Yousab Grees, Polina Iaremchuk, Ramtin Ehsani +3
Commit messages in a version control system provide valuable information for developers regarding code changes in software systems. Commit messages can be the only source of inform…
How Safe Are AI-Generated Patches? A Large-scale Study on Security Risks in LLM and Agentic Automated Program Repair on SWE-bench
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
Large language models (LLMs) and their agentic frameworks are increasingly adopted to perform development tasks such as automated program repair (APR). While prior work has identif…
Hierarchical Knowledge Injection for Improving LLM-based Program Repair
Ramtin Ehsani, Esteban Parra, Sonia Haiduc +1
Prompting LLMs with bug-related context (e.g., error messages, stack traces) improves automated program repair, but many bugs still remain unresolved. In real-world projects, devel…
What Characteristics Make ChatGPT Effective for Software Issue Resolution? An Empirical Study of Task, Project, and Conversational Signals in GitHub Issues
Ramtin Ehsani, Sakshi Pathak, Esteban Parra +2
Conversational large-language models are extensively used for issue resolution tasks. However, not all developer-LLM conversations are useful for effective issue resolution. In thi…
Psycholinguistic Analyses in Software Engineering Text: A Systematic Literature Review
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
Context: A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channe…
Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions
Amirali Sajadi, Binh Le, Anh Nguyen +2
The widespread adoption of conversational LLMs for software development has raised new security concerns regarding the safety of LLM-generated content. Our motivational study outli…
Understanding and Predicting Derailment in Toxic Conversations on GitHub
Mia Mohammad Imran, Robert Zita, Rebekah Copeland +3
Software projects thrive on the involvement and contributions of individuals from different backgrounds. However, toxic language and negative interactions can hinder the participat…
Towards Detecting Prompt Knowledge Gaps for Improved LLM-guided Issue Resolution
Ramtin Ehsani, Sakshi Pathak, Preetha Chatterjee
Large language models (LLMs) have become essential in software development, especially for issue resolution. However, despite their widespread use, significant challenges persist i…
Analyzing Toxicity in Open Source Software Communications Using Psycholinguistics and Moral Foundations Theory
Ramtin Ehsani, Rezvaneh Rezapour, Preetha Chatterjee
Studies have shown that toxic behavior can cause contributors to leave, and hinder newcomers' (especially from underrepresented communities) participation in Open Source Software (…