RepoRepair: Leveraging Code Documentation for Repository-Level Automated Program Repair

Automatic program repair can be a challenging task, especially when resolving complex issues at a repository-level, which often involves issue reproduction, fault localization, code repair, testing and validation. Issues of this scale can be commonly found in popular GitHub repositories or datasets that are derived from them. Some repository-level approaches separate localization and repair into distinct phases. Where this is the case, the fault localization approaches vary in terms of the granularity of localization. Where the impact of granularity is explored to some degree for smaller datasets, not all isolate this issue from the separate question of localization accuracy by testing code repair under the assumption of perfect fault localization. To the best of the authors' knowledge, no repository-scale studies have explicitly investigated granularity under this assumption, nor conducted a systematic empirical comparison of granularity levels in isolation. We propose a framework for performing such tests by modifying the localization phase of the Agentless framework to retrieve ground-truth localization data and include this as context in the prompt fed to the repair phase. We show that under this configuration and as a generalization over the SWE-Bench-Mini dataset, function-level granularity yields the highest repair rate against line-level and file-level. However, a deeper dive suggests that the ideal granularity may in fact be task dependent. This study is not intended to improve on the state-of-the-art, nor do we intend for results to be compared against any complete agentic frameworks. Rather, we present a proof of concept for investigating how fault localization may impact automatic code repair in repository-scale scenarios. We present preliminary findings to this end and encourage further research into this relationship between the two phases.

Repository-grounded automated repair is often reported as a single end-to-end capability, which hides distinct failure modes such as poor file targeting, incorrect patch synthesis, and failed iterative debugging. We present Loc2Repair, a modular evaluation framework for controlled analysis of repository-grounded repair pipelines, and use it to isolate file-level issue localization as an upstream variable. Loc2Repair decouples localization and repair under a shared runtime, artifact schema, and evaluation harness, allowing researchers to combine different localization models and repair backbones under matched conditions. Using three repair backbones on SWE-bench Verified, we compare baseline repair without explicit localization, repair guided by predicted localization from two localizers, and repair guided by gold modified-file sets. Explicit localization consistently improves resolved rate across all backbones: pooled performance increases from 44.7% for baseline repair to 48.9% and 49.1% with predicted localization, and to 52.4% with gold localization. Localization also reduces mean elapsed time overall: in pooled paired analysis, mean elapsed time decreases by 100.94 s and 52.25 s for the two predicted-localization settings, and by 154.45 s with gold guidance, although token effects remain heterogeneous across models. Overall, Loc2Repair shows file-level localization is a consistent repair lever, improving effectiveness and mean latency in pooled analysis, while gold-guided failures expose headroom beyond localization.

Recent 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. Second, more memory does not monotonically lead to higher repair success, suggesting that relevance, quality, and redundancy matter more than raw memory volume. Third, memory accumulation is phase-misaligned: repositories may contain many reproduction experiences but few patch or refinement experiences. To address these problems, we propose an adaptive experience retrieval framework for repository-level program repair. Our framework introduces coverage-aware retrieval, which falls back to cross-repository or repair-type-based memories when same-repository memory is insufficient; quality-aware selection, which ranks memories by relevance, historical utility, specificity, and redundancy; and stage-aware routing, which separates and retrieves memories for reproduction, localization, patch generation, patch refinement, and validation. Evaluated on SWE-Bench-Lite and SWE-Bench-Verified, the proposed framework improves repair performance on under-covered repositories, reduces noisy memory retrieval, and better supports failed-to-fixed patch refinement. Our results show that the key to memory-augmented repair is not simply accumulating more experiences, but retrieving the right experiences for the right repair context.