I am a PhD Candidate and Graduate Assistant in the Department of Computer Science at the University of Nevada, Las Vegas (UNLV), working in the Software Evolution (EVOL) Lab under the supervision of Professor John Businge. I hold an MSc in Computer Science from Makerere University, Uganda (advised by Dr. Joyce Nakatumba-Nabende and Prof. Michel R.V. Chaudron) and a BSc in Computer Science from Gulu University, Uganda. My research lies at the intersection of Empirical Software Engineering and AI for Software Engineering, with publications in venues including ASE, SCAM, MSR, AIware (co-located with FSE), the Empirical Software Engineering (EMSE) journal, and SEDE. I serve the research community as a Junior PC member for MSR 2026, a PC member for ESEC/FSE 2026 (Ideas, Visions, and Reflections track), and a reviewer for Springer Nature Scientific Reports, Elsevier Data in Brief, and the Journal of Advances in Information Technology.
"And you will know the truth, and the truth will set you free." — John 8:32
Daniel Ogenrwot, John Businge
AIware 2026 — Benchmark & Dataset Track (co-located with FSE 2026) 2026
The rapid adoption of large language models (LLMs) like ChatGPT has introduced new dynamics in software development, particularly within pull request workflows. We analyze 338 pull requests from 255 GitHub repositories containing self-admitted ChatGPT usage, comprising 645 AI-generated snippets and 3,486 developer-authored patches.
Daniel Ogenrwot, John Businge
AIware 2026 — Benchmark & Dataset Track (co-located with FSE 2026) 2026
The rapid adoption of large language models (LLMs) like ChatGPT has introduced new dynamics in software development, particularly within pull request workflows. We analyze 338 pull requests from 255 GitHub repositories containing self-admitted ChatGPT usage, comprising 645 AI-generated snippets and 3,486 developer-authored patches.
Daniel Ogenrwot, John Businge
AIware 2026 — Benchmark & Dataset Track (co-located with FSE 2026) 2026
Large-scale dataset of 142K+ agentic pull requests from 59K+ repositories, identifying 29K PRs with merge conflicts and extracting 336K+ fine-grained conflict regions across AI coding agents (Copilot, Devin, Cursor, and others).
Daniel Ogenrwot, John Businge
AIware 2026 — Benchmark & Dataset Track (co-located with FSE 2026) 2026
Large-scale dataset of 142K+ agentic pull requests from 59K+ repositories, identifying 29K PRs with merge conflicts and extracting 336K+ fine-grained conflict regions across AI coding agents (Copilot, Devin, Cursor, and others).
Daniel Ogenrwot, John Businge
2025 IEEE International Conference on Source Code Analysis & Manipulation (SCAM) 2025 Distinguished Artifact Award
RePatch addresses structural drift in divergent software variants through refactoring-aware patch integration, achieving 52.8% success on previously failing patches.
Daniel Ogenrwot, John Businge
2025 IEEE International Conference on Source Code Analysis & Manipulation (SCAM) 2025 Distinguished Artifact Award
RePatch addresses structural drift in divergent software variants through refactoring-aware patch integration, achieving 52.8% success on previously failing patches.
Daniel Ogenrwot, John Businge
39th IEEE/ACM International Conference on Automated Software Engineering (ASE) 2024
Empirical study investigating how ChatGPT-generated patches influence pull request acceptance decisions, examining 338 pull requests across open-source projects.
Daniel Ogenrwot, John Businge
39th IEEE/ACM International Conference on Automated Software Engineering (ASE) 2024
Empirical study investigating how ChatGPT-generated patches influence pull request acceptance decisions, examining 338 pull requests across open-source projects.