← 研踪 PaperTrailarXiv:2611.01292 · v1
Causality-Based Fault Localization
首次提交 2026-11-01 05:48
版本更新 2026-11-01 05:48
雷达观测 2026-11-04 23:43
摘要
We introduce Causality-Based Fault Localization (CBFL), a novel approach to fault localization grounded in the theory of actual causality. We use the notion of causal responsibility to formalize and define the contribution of a given location in the source code under the context of a failing test. We also utilize a distance metric to prioritize minimal fixes and penalize broken tests, leading to more interpretable and fixable fault localization. We implemented CBFL in a tool, SoftReX, and our evaluation results demonstrate that SoftReX outperforms state-of-the-art Mutation-Based Fault Localization (MBFL) tools based on Mean Reciprocal Rank (MRR) and Top-1 by 16% on both metrics, when using the same set of mutants. We also propose a sophisticated mutation generation strategy based on the structure and representation of the program to improve the accuracy of our ranked list, which outperforms state-of-the-art methods by 2.6x in terms of required mutants evaluated while remaining competitive in performance.
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v1 · 2026-11-01 05:48 · 最新观测版本
Causality-Based Fault Localization
We introduce Causality-Based Fault Localization (CBFL), a novel approach to fault localization grounded in the theory of actual causality. We use the notion of causal responsibility to formalize and define the contribution of a given location in the source code under the context of a failing test. We also utilize a distance metric to prioritize minimal fixes and penalize broken tests, leading to more interpretable and fixable fault localization. We implemented CBFL in a tool, SoftReX, and our evaluation results demonstrate that SoftReX outperforms state-of-the-art Mutation-Based Fault Localization (MBFL) tools based on Mean Reciprocal Rank (MRR) and Top-1 by 16% on both metrics, when using the same set of mutants. We also propose a sophisticated mutation generation strategy based on the structure and representation of the program to improve the accuracy of our ranked list, which outperforms state-of-the-art methods by 2.6x in terms of required mutants evaluated while remaining competitive in performance.
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