PhD Candidate

Daniel Ogenrwot

Daniel's work studies how AI systems are changing collaborative software development. He focuses on pull request behavior, merge conflicts, and patch acceptance dynamics to build grounded evidence for AI-enabled engineering practices.

Research Interests

AI-assisted software development and code review outcomes.
Pull request analytics, merge conflicts, and patch adoption.
Empirical studies of developer behavior and collaboration signals.
Open datasets for software engineering research reproducibility.

Current Research Projects

AgenticFlict: dataset construction and analysis of AI-agent merge conflicts.
PatchTrack: longitudinal analysis of ChatGPT-influenced pull requests.
Comparative studies of AI-authored and human-authored change patterns.
Benchmark pipelines for mining and validating large-scale PR metadata.