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.
Dlfix: context-based code transformation learning for automated program repair,
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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SpecTune improves LLM-based automated program repair by deriving localized postconditions at execution checkpoints and using alpha and beta signals to produce precise fault-localization and patch-generation guidance.
PrevaRank ranks plausible patches from APR tools using similarity to historic fix features, improving correct fix placement in top ranks on Defects4J bugs.
RAVEN combines agentic RAG, iterative repair, and a cross-file Curator Agent to achieve 83.13% repair success on diverse real-world CVEs using local open-source LLMs.
MultiMend augments buggy function context via retrieval and generates multi-hunk patches, fixing 2,227 of 5,501 bugs across six benchmarks in four languages.
citing papers explorer
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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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Enhancing Program Repair with Specification Guidance and Intermediate Behavioral Signals
SpecTune improves LLM-based automated program repair by deriving localized postconditions at execution checkpoints and using alpha and beta signals to produce precise fault-localization and patch-generation guidance.
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Ranking Plausible Patches by Historic Feature Frequencies
PrevaRank ranks plausible patches from APR tools using similarity to historic fix features, improving correct fix placement in top ranks on Defects4J bugs.
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RAVEN: Agentic RAG for Automated Vulnerability Repair
RAVEN combines agentic RAG, iterative repair, and a cross-file Curator Agent to achieve 83.13% repair success on diverse real-world CVEs using local open-source LLMs.
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MultiMend: Multilingual Program Repair with Context Augmentation and Multi-Hunk Patch Generation
MultiMend augments buggy function context via retrieval and generates multi-hunk patches, fixing 2,227 of 5,501 bugs across six benchmarks in four languages.