RepairAgent autonomously repairs 164 bugs on Defects4J including 39 not fixed by prior techniques by treating an LLM as an agent that invokes tools via a finite state machine and dynamic prompts.
Selfapr: Self-supervised program repair with test execution diagnostics
5 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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A catalog of ten cache smells in GitLab CI/CD, an automated detector achieving 0.98 F1, and empirical evidence that the smells appear in 89% of 228 mature open-source projects.
EvidenT repairs 53.88% of real-world RISC-V system-level package build failures by preserving repair history and build artifacts in a closed-loop validation system, outperforming baselines by a wide margin.
SpecTune improves LLM-based automated program repair by validating localized postconditions and using their intermediate violations for more precise fault localization and patching.
citing papers explorer
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RepairAgent: An Autonomous, LLM-Based Agent for Program Repair
RepairAgent autonomously repairs 164 bugs on Defects4J including 39 not fixed by prior techniques by treating an LLM as an agent that invokes tools via a finite state machine and dynamic prompts.
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Cache-Related Smells in GitLab CI/CD: Comprehensive Catalog, Automated Detection, and Empirical Evidence
A catalog of ten cache smells in GitLab CI/CD, an automated detector achieving 0.98 F1, and empirical evidence that the smells appear in 89% of 228 mature open-source projects.
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EvidenT: An Evidence-Preserving Framework for Iterative System-Level Package Repair
EvidenT repairs 53.88% of real-world RISC-V system-level package build failures by preserving repair history and build artifacts in a closed-loop validation system, outperforming baselines by a wide margin.
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Enhancing Program Repair with Specification Guidance and Intermediate Behavioral Signals
SpecTune improves LLM-based automated program repair by validating localized postconditions and using their intermediate violations for more precise fault localization and patching.
- What Breaks When LLMs Code? Characterizing Operational Safety Failures of Agentic Code Assistants