SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
Soremekun, Sudipta Chattopadhyay, Emamurho Ugherughe, and Andreas Zeller
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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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Similar Pattern Annotation via Retrieval Knowledge for LLM-Based Test Code Fault Localization
SPARK improves LLM-based test code fault localization by retrieving similar past faults and selectively annotating suspicious lines in new failing tests.
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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.