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

2025-11-28

AI Summary

In mutation-based fault localization (MBFL), spurious mutant-killing relationships—i.e., “false kills”—introduce significant noise into the kill matrix, severely degrading fault localization accuracy. To address this, we propose the first signal-processing–based kill-matrix denoising framework: it first constructs a hybrid kill matrix to enhance signal quality, then applies frequency-domain filtering to suppress noise at its source—not merely post-hoc correction. Our method integrates signal enhancement, frequency-domain denoising, and fuzzy-value-based suspiciousness computation. Evaluated on Defects4J v2.0.0, it achieves a Top-1 localization success rate of 129 defects—substantially outperforming BLMu (85) and Delta4Ms (103)—with only 0.11 seconds of overhead, incurring negligible runtime cost. The core contribution lies in the systematic introduction of signal denoising theory into MBFL, enabling proactive noise suppression and concurrent improvement in localization precision.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Reduces noise in mutation-based fault localization to improve accuracy.
• Refines the kill matrix using signal processing to enhance fault detection.
• Addresses false kill relationships between mutants and tests for better debugging.

Innovation

• Refines kill matrix using signal processing denoising techniques
• Employs hybrid matrix construction and frequency domain filtering
• Introduces MBFL-DKMR framework with fuzzy values for fault localization