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This paper introduces HyperFL, a novel query-adaptive representation learning framework designed to enhance software fault localization by generating dynamic query-specific parameters for issue report embeddings. By utilizing a lightweight hypernetwork, HyperFL adapts to the diverse characteristics of real-world issue reports while maintaining a fixed code encoder, leading to significant improvements in retrieval performance. Experimental results show that HyperFL outperforms the state-of-the-art method SweRank, achieving up to 13.3% relative improvement in function-level MRR@10 and 16.7% in Hit@1, demonstrating the effectiveness of its approach in addressing the variability in issue report structures.
HyperFL achieves up to 16.7% better fault localization accuracy by adapting to the unique characteristics of diverse issue reports in real-time.
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.
