Repository-level bug localization-the task of identifying where code must be modified to fix a bug-is a critical software engineering challenge. Standard Large Language Modles (LLMs) are often unsuitable for this task due to context window limitations that prevent them from processing entire code repositories. As a result, various retrieval methods are commonly used, including keyword matching, text similarity, and simple graph-based heuristics such as Breadth-First Search. Graph Neural Networks (GNNs) offer a promising alternative due to their ability to model complex, repository-wide dependencies; however, their application has been hindered by the lack of a dedicated benchmark. To address this gap, we introduce GREPO, the first GNN benchmark for repository-scale bug localization tasks. GREPO comprises 86 Python repositories and 47294 bug-fixing tasks, providing graph-based data structures ready for direct GNN processing. Our evaluation of various GNN architectures shows outstanding performance compared to established information retrieval baselines. This work highlights the potential of GNNs for bug localization and established GREPO as a foundation resource for future research, The code is available at https://github.com/qingpingmo/GREPO.

翻译：仓库级缺陷定位——即识别需要修改哪些代码以修复缺陷的任务——是一项关键的软件工程挑战。标准的大型语言模型（LLMs）由于上下文窗口限制而无法处理整个代码仓库，通常不适用于此任务。因此，通常采用各种检索方法，包括关键词匹配、文本相似度以及简单的基于图的启发式方法（如广度优先搜索）。图神经网络（GNNs）因其能够建模复杂的、仓库范围内的依赖关系而成为一种有前景的替代方案；然而，由于缺乏专用基准，其应用一直受到阻碍。为填补这一空白，我们提出了GREPO，这是首个面向仓库级缺陷定位任务的GNN基准。GREPO包含86个Python仓库和47294个缺陷修复任务，提供了可直接用于GNN处理的基于图的数据结构。我们对多种GNN架构的评估表明，相较于现有的信息检索基线方法，其性能表现优异。这项工作凸显了GNN在缺陷定位任务中的潜力，并将GREPO确立为未来研究的基础资源。代码可在https://github.com/qingpingmo/GREPO获取。