7 citations · 25 across the 29 of their papers we have counts for
29 papers
An Empirical Analysis of CodeQL False Positives and Query Refinements for Java Vulnerabilities
Amirali Sajadi, Saikat Dutta, Preetha Chatterjee
Static application security testing (SAST) tools help developers find vulnerabilities before deployment, but false positives create substantial triage effort. We study whether Code…
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…
TraceView: Interactive Visualization of Agentic Program Repair Trajectories
Amirali Sajadi, Tu Nguyen, Kimmie Huynh +2
LLM-based automated program repair (APR) agents generate patches to fix software bugs with minimal human intervention. These agents often produce long trajectories of reasoning, to…
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…
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…
AXE: Grey-Box Exploitability Confirmation for Localized Vulnerability Reports
Amirali Sajadi, Tu Nguyen, Kostadin Damevski +1
Vulnerability detection tools are widely adopted in software projects, yet they often overwhelm maintainers with false positives and non-actionable reports. Automated exploitation…
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…
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 (…
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…
Shedding Light on Software Engineering-specific Metaphors and Idioms
Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski
Use of figurative language, such as metaphors and idioms, is common in our daily-life communications, and it can also be found in Software Engineering (SE) channels, such as commen…
Exploring ChatGPT for Toxicity Detection in GitHub
Shyamal Mishra, Preetha Chatterjee
Fostering a collaborative and inclusive environment is crucial for the sustained progress of open source development. However, the prevalence of negative discourse, often manifeste…
Uncovering the Causes of Emotions in Software Developer Communication Using Zero-shot LLMs
Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski
Understanding and identifying the causes behind developers' emotions (e.g., Frustration caused by `delays in merging pull requests') can be crucial towards finding solutions to pro…
Interpersonal Trust in OSS: Exploring Dimensions of Trust in GitHub Pull Requests
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
Interpersonal trust plays a crucial role in facilitating collaborative tasks, such as software development. While previous research recognizes the significance of trust in an organ…
Towards Understanding Emotions in Informal Developer Interactions: A Gitter Chat Study
Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee
Emotions play a significant role in teamwork and collaborative activities like software development. While researchers have analyzed developer emotions in various software artifact…