1 citations · 1 across the 7 of their papers we have counts for
4 papers · 2 filters
Prompt Quality and Pull Request Outcomes: A Stage-Based Empirical Study of LLM-Assisted Development
Richard Sserunjogi, Daniel Ogenrwot, John Businge
Large language model (LLM)-powered tools such as ChatGPT are increasingly used in collaborative software engineering workflows, yet little is known about how prompt structure influ…
AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub
Daniel Ogenrwot, John Businge
Software Engineering 3.0 marks a paradigm shift in software development, in which AI coding agents are no longer just assistive tools but active contributors. While prior empirical…
MOVis: A Visual Analytics Tool for Surfacing Missed Patches Across Software Variants
Jorge Gonzalo Delgado Cervantes, John Businge, Daniel Ogenrwot
Clone-and-own development produces families of related software variants that evolve independently. As variants diverge, important fixes applied in one repository are often missing…
How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests
Daniel Ogenrwot, John Businge
AI coding agents are increasingly acting as autonomous contributors by generating and submitting pull requests (PRs). However, we lack empirical evidence on how these agent-generat…