SGAgent: Suggestion-Guided LLM-Based Multi-Agent Framework for Repository-Level Software Repair
arXiv 2602.23647•56a24d72a09e7cdbf9f32e0afd2bdb68689dc5672a713d796f9480cc0868bfa9
IC3/PDRKV-cache-memoryLLM-based-repairSWE-rebenchdatasetsembodied-planningfailure-attributionflaky-testsformal-verificationfunctional-test-generationfuzzingmicroservice-testingmulti-agent-systemsreinforcement-learningsecure-code-generationseed-selectionvulnerability-repair
Paper metadata
- arXiv ID
- 2602.23647
- Version
- Not specified by this published record
- Category
- Computer Science — Software Engineering (cs.SE)
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Evidence and limitations
- Source ID
- arxiv_cs_se
- Record identifier
- 56a24d72a09e7cdbf9f32e0afd2bdb68689dc5672a713d796f9480cc0868bfa9
- Enrichment time
- 2026-03-04T20:21:24Z
- AI-assisted enrichment
- Yes
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