HyperFL：面向软件故障定位的查询自适应表示学习

中文摘要 AI 辅助

软件故障定位用于识别导致报告问题的代码位置，是自动化调试和程序修复的基础步骤。近期基于检索的方法将故障定位建模为密集检索任务，通过学习问题报告与源代码之间的共享嵌入空间实现。然而，这些方法使用固定的查询表示编码所有问题报告，而实际问题报告在长度、结构和调试信息上存在显著多样性。为解决这一局限，我们提出HyperFL，一种面向软件故障定位的查询自适应表示学习框架。HyperFL采用轻量级超网络为查询编码器生成特定查询的LoRA参数，在保持代码编码器固定可复用的同时实现动态查询自适应。在真实世界的问题定位基准上开展的实验表明，HyperFL在多种嵌入骨干网络上均能提升检索性能，相较于当前最优方法SweRank，其在函数级MRR@10上实现了最高13.3%的相对提升，在Hit@1上实现了最高16.7%的相对提升。进一步分析显示，HyperFL针对不同问题特征学习出 distinct 的自适应模式，凸显了查询自适应表示在软件问题定位中的有效性。

英文摘要

Software fault localization identifies the code locations responsible for reported issues and is a fundamental step toward automated debugging and program repair. Recent retrieval-based approaches formulate fault localization as a dense retrieval task by learning a shared embedding space between issue reports and source code. However, these methods encode all issue reports using a fixed query representation, despite the substantial diversity of real-world issue reports in length, structure, and debugging information. To address this limitation, we propose HyperFL, a query-adaptive representation learning framework for software fault localization. HyperFL employs a lightweight hypernetwork to generate query-specific LoRA parameters for the query encoder, enabling dynamic query adaptation while keeping the code encoder fixed and reusable. Experiments on a real-world issue localization benchmark demonstrate that HyperFL consistently improves retrieval performance across multiple embedding backbones, achieving up to 13.3% relative improvement in function-level MRR@10 and 16.7% relative improvement in Hit@1 over the state-of-the-art method SweRank. Further analysis shows that HyperFL learns distinct adaptation patterns for different issue characteristics, highlighting the effectiveness of query-adaptive representations for software issue localization.

发表机构

• University of Connecticut（康涅狄格大学）
• University of Cincinnati（辛辛那提大学）

机构由 AI 辅助整理，请以论文原文为准。