Quantitative partial equivalence analysis quantifies behavioral differences between original and patched programs via symbolic analysis and a range-based heuristic for numerical domains.
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AgenticFlict is a public dataset of 29K+ textual merge conflicts from AI agent PRs, collected via merge simulation on 107K processed PRs and showing a 27.67% conflict rate with variation across agents.
AI coding agents produce pull requests with substantially more commits and slightly higher description-to-diff similarity than human developers, based on analysis of 29,095 merged PRs.
citing papers explorer
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Quantitative Symbolic Patch Impact Analysis
Quantitative partial equivalence analysis quantifies behavioral differences between original and patched programs via symbolic analysis and a range-based heuristic for numerical domains.
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AgenticFlict: A Large-Scale Dataset of Merge Conflicts in AI Coding Agent Pull Requests on GitHub
AgenticFlict is a public dataset of 29K+ textual merge conflicts from AI agent PRs, collected via merge simulation on 107K processed PRs and showing a 27.67% conflict rate with variation across agents.
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How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests
AI coding agents produce pull requests with substantially more commits and slightly higher description-to-diff similarity than human developers, based on analysis of 29,095 merged PRs.