Signal Denoising Based Kill Matrix Refinement for Mutation-Based Fault Localization

Abstract

Software debugging is a critical and time-consuming aspect of software development, with fault localization being a fundamental step that significantly impacts debugging efficiency. Mutation-Based Fault Localization (MBFL) has gained prominence due to its robust theoretical foundations and fine-grained analysis capabilities. However, recent studies have identified a critical challenge: noise phenomena, specifically the false kill relationships between mutants and tests, which significantly degrade localization effectiveness. While several approaches have been proposed to rectify the final localization results, they do not directly address the underlying noise. In this paper, we propose a novel approach to refine the kill matrix, a core data structure capturing mutant-test relationships in MBFL, by treating it as a signal that contains both meaningful fault-related patterns and high-frequency noise. Inspired by signal processing theory, we introduce DKMR (Denoising-based Kill Matrix Refinement), which employs two key stages: (1) signal enhancement through hybrid matrix construction to improve the signal-to-noise ratio for better denoising, and (2) signal denoising via frequency domain filtering to suppress noise while preserving fault-related patterns. Building on this foundation, we develop MBFL-DKMR, a fault localization framework that utilizes the refined matrix with continuous values for suspiciousness calculation. Our evaluation on Defects4J v2.0.0 demonstrates that MBFL-DKMR effectively mitigates the noise and outperforms both state-of-the-art baselines (BLMu, Delta4Ms, and SMARTFL) and representative traditional baselines (MBFLME, MBFLMU, and SBFL). Specifically, MBFL-DKMR localizes 141 faults at Top-1, compared to 113 for BLMu, 112 for Delta4Ms, and 101 for SMARTFL, while introducing negligible additional computational overhead (0.15 seconds, 0.0015% of total time).