Rover uses a new Multi-layer Code Property Graph and clustering to supply LLMs with dependency-aware contexts, outperforming standalone LLMs, MergeGen, and WizardMerge on similarity to ground-truth conflict resolutions.
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LogicLoc combines LLMs with Datalog to achieve accurate repo-level code localization without relying on keyword shortcuts in benchmarks.
Coding agents reached 22-29% adoption in GitHub projects within months of release, with agent-assisted commits larger and focused on features and bug fixes.
A large-scale empirical study categorizes bugs in LLM agents and demonstrates that a specialized LLM agent can annotate them accurately at very low cost.
More fault localization context does not consistently improve LLM-based program repair; file-level context gives 15-17x gains, optimal around 6-10 files, while line-level context often degrades performance from noise.
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
AutoRocq is an LLM agent that learns proofs on-the-fly by collaborating with the Rocq prover to verify programs on SV-COMP benchmarks and Linux kernel modules.
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.
citing papers explorer
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Rover: Context-aware Conflict Resolution with LLM
Rover uses a new Multi-layer Code Property Graph and clustering to supply LLMs with dependency-aware contexts, outperforming standalone LLMs, MergeGen, and WizardMerge on similarity to ground-truth conflict resolutions.
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Neurosymbolic Repo-level Code Localization
LogicLoc combines LLMs with Datalog to achieve accurate repo-level code localization without relying on keyword shortcuts in benchmarks.
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Agentic Much? Adoption of Coding Agents on GitHub
Coding agents reached 22-29% adoption in GitHub projects within months of release, with agent-assisted commits larger and focused on features and bug fixes.
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When Agents Fail: A Comprehensive Study of Bugs in LLM Agents with Automated Labeling
A large-scale empirical study categorizes bugs in LLM agents and demonstrates that a specialized LLM agent can annotate them accurately at very low cost.
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On the Role of Fault Localization Context for LLM-Based Program Repair
More fault localization context does not consistently improve LLM-based program repair; file-level context gives 15-17x gains, optimal around 6-10 files, while line-level context often degrades performance from noise.
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Beyond Fixed Tests: Repository-Level Issue Resolution as Coevolution of Code and Behavioral Constraints
Agent-CoEvo is a multi-agent LLM framework that coevolves code patches and test patches to resolve repository-level issues, outperforming fixed-test baselines on SWE-bench Lite and SWT-bench Lite.
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Agentic Verification of Software Systems
AutoRocq is an LLM agent that learns proofs on-the-fly by collaborating with the Rocq prover to verify programs on SV-COMP benchmarks and Linux kernel modules.
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Towards Explorative IRBL: Combining Semantic Retrieval with LLM-driven Iterative Code Exploration
GenLoc integrates semantic retrieval and LLM-based iterative code exploration to outperform prior IRBL and LLM methods on Java and Python bug localization benchmarks.