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

TL;DR

This paper introduces MBFL-DKMR, a novel fault localization method that refines the kill matrix using signal processing techniques to reduce noise and improve accuracy in identifying faults.

Contribution

It proposes a denoising-based refinement of the kill matrix for mutation-based fault localization, enhancing fault detection accuracy with minimal overhead.

Findings

Outperforms state-of-the-art MBFL techniques on Defects4J v2.0.0

Localized 129 faults at Top-1, surpassing BLMu and Delta4Ms

Negligible additional computational overhead (0.11 seconds)

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.