11 papers
Think Harder and Don't Overlook Your Options: Revisiting Issue-Commit Linking with LLM-Assisted Retrieval
Cole Morgan, Muhammad Asaduzzaman, Shaiful Chowdhury +1
Linking issue reports to the commits that resolve them is essential for software traceability, maintenance, and evolution. Accurate issue-commit links help developers to understand…
The Repeat Offenders: Characterizing and Predicting Extremely Bug-Prone Source Methods
Ethan Friesen, Sasha Morton-Salmon, Md Nahidul Islam Opu +2
Bug prediction has long been considered the "prince" of empirical software engineering research, and accordingly, a substantial body of work has focused on predicting bugs to enabl…
How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study
Tanjum Motin Mitul, Md. Masud Mazumder, Md Nahidul Islam Opu +1
As Software Engineering enters its new era (SE 3.0), AI coding agents increasingly automate software development workflows. However, it remains unclear how exactly these agents rec…
How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests
Md Nahidul Islam Opu, Shahidul Islam, Muhammad Asaduzzaman +1
LLM-based software engineering is influencing modern software development. In addition to correctness, prior studies have also examined the performance of software artifacts genera…
A First Look at the Self-Admitted Technical Debt in Test Code: Taxonomy and Detection
Shahidul Islam, Md Nahidul Islam Opu, Shaowei Wang +1
Self-admitted technical debt (SATD) refers to comments in which developers explicitly acknowledge code issues, workarounds, or suboptimal solutions. SATD is known to significantly…
LLM-Based Detection of Tangled Code Changes for Higher-Quality Method-Level Bug Datasets
Md Nahidul Islam Opu, Shaowei Wang, Shaiful Chowdhury
Tangled code changes, commits that conflate unrelated modifications such as bug fixes, refactorings, and enhancements, introduce significant noise into bug datasets and adversely a…