AdaRepair-Mem: Adaptive Experience Orchestration for Repository-Level Program Repair
AdaRepair-Mem introduces coverage-, quality-, and stage-aware memory retrieval that improves LLM-based repository-level program repair on SWE-Bench.
The paper identifies three limitations in existing repository-level memory retrieval for LLM-based issue resolution: imbalanced episodic memory across repositories, non-monotonic gains from memory volume, and phase-misaligned memory accumulation. The proposed framework adds coverage-aware retrieval with cross-repository fallback, quality-aware selection ranking relevance, utility, specificity, and redundancy, and stage-aware routing across reproduction, localization, patch generation, refinement, and validation. On SWE-Bench-Lite and SWE-Bench-Verified it improves repair of under-covered repositories and reduces noisy retrieval.
- Coverage-aware retrieval falls back to cross-repository or repair-type memories
- Quality-aware selection ranks memories by relevance, utility, specificity, redundancy
- Stage-aware routing separates memory per repair phase
- Evaluated on SWE-Bench-Lite and SWE-Bench-Verified
- Products
- AdaRepair-MemSWE-Bench
From the original article
1 min read at arxiv.orgRecent memory-augmented repository-level program repair methods reuse historical repair experiences to improve LLM-based issue resolution. However, our analysis reveals three limitations in existing repository-level memory retrieval. First, episodic memory is highly imbalanced across repositories, leaving low-resource repositories with little effective support.
By Z. C. Luo · arxiv.org
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