MBFL-DKMR: Improving Mutation-based Fault Localization through Denoising-based Kill Matrix Refinement

TL;DR

This work addresses noise in mutation-based fault localization by treating the kill matrix as a 2D signal and applying a two-stage denoising process (signal enhancement followed by frequency-domain filtering) to refine kill information. The DKMR method is integrated into MBFL to produce MBFL-DKMR, which uses fuzzy suspiciousness scores for more nuanced fault localization. Evaluations on Defects4J v2.0.0 show that DKMR reduces noise and MBFL-DKMR achieves notable improvements in Top-1, MAP, and EXAM metrics while incurring negligible overhead. The results demonstrate the practical potential of applying signal processing techniques to MBFL to enhance localization effectiveness without sacrificing efficiency.

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 fuzzy values for suspiciousness calculation. Our evaluation on Defects4J v2.0.0 demonstrates that MBFL-DKMR effectively mitigates the noise and outperforms the state-of-the-art MBFL techniques. Specifically, MBFL-DKMR achieves 129 faults localized at Top-1 compared to 85 for BLMu and 103 for Delta4Ms, with negligible additional computational overhead (0.11 seconds, 0.001\% of total time). This work highlights the potential of signal processing techniques to enhance the effectiveness of MBFL by refining the kill matrix.

Figures (3)

• Figure 1: Workflow of MBFL-DKMR
• Figure 2: Effectiveness Evaluation of MBFL-DKMR in terms of EXAM distribution
• Figure 3: Comparison between MBFL-DKMR and state-of-the-art MBFL Techniques in terms of EXAM distribution