2 citations · 5 across the 12 of their papers we have counts for
11 papers · 1 filter
How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair
Ramtin Ehsani, Irene Manotas, Saurabh Pujar +2
Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent. Even when provided with the same contextual inf…
Faster Code, Deeper Debt? A Multivocal Literature Review on Technical Debt and Its Early Signs in LLM-Assisted Software Development
Ramtin Ehsani, Shriya Rawal, Yuanfang Cai +1
With the rapid adoption of LLM-assisted coding, the need to manage the technical debt these systems introduce has become urgent. In this paper, we conduct a multivocal literature r…
Towards A Sustainable Future for Peer Review in Software Engineering
Esteban Parra, Sonia Haiduc, Preetha Chatterjee +2
Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software…
Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub
Ramtin Ehsani, Sakshi Pathak, Shriya Rawal +3
AI coding agents are now submitting pull requests (PRs) to software projects, acting not just as assistants but as autonomous contributors. As these agentic contributions are rapid…
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…
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…
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 (…
Incivility in Open Source Projects: A Comprehensive Annotated Dataset of Locked GitHub Issue Threads
Ramtin Ehsani, Mia Mohammad Imran, Robert Zita +2
In the dynamic landscape of open source software (OSS) development, understanding and addressing incivility within issue discussions is crucial for fostering healthy and productive…
Exploring Moral Principles Exhibited in OSS: A Case Study on GitHub Heated Issues
Ramtin Ehsani, Rezvaneh Rezapour, Preetha Chatterjee
To foster collaboration and inclusivity in Open Source Software (OSS) projects, it is crucial to understand and detect patterns of toxic language that may drive contributors away,…