An Empirical Study of Fault Localization Families and Their Combinations
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Abstract (arXiv)
The performance of fault localization techniques is critical to their adoption in practice. This paper reports on an empirical study of a wide range of fault localization techniques on real-world faults. Different from previous studies, this paper (1) considers a wide range of techniques from different families, (2) combines different techniques, and (3) considers the execution time of different techniques. Our results reveal that a combined technique significantly outperforms any individual technique (200% increase in faults localized in Top 1), suggesting that combination may be a desirable way to apply fault localization techniques and that future techniques should also be evaluated in the combined setting. Our implementation is publicly available for evaluating and combining fault localization techniques.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 1; 2024: 3; 2025: 2; 2026: 2.
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- 2024CodeR: Issue Resolving with Multi-Agent and Task Graphscited by 89
- 2024HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scalecited by 61
- 2025Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysiscited by 33
- 2021Black-Box Testing of Deep Neural Networks Through Test Case Diversitycited by 18
- 2022DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programscited by 16
- 2022Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5cited by 9
- 2024A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexioncited by 8
- 2023Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systemscited by 1
- 2025Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Tracescited by 1
- 2022BigIssue: A Realistic Bug Localization Benchmarkcited by 0
- 2022Infrared: A Meta Bug Detectorcited by 0
- 2026Improving MPI Error Detection and Repair with Large Language Models and Bug Referencescited by 0
- 2026From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning29 Sep 2026
- 2026Improving MPI Error Detection and Repair with Large Language Models and Bug References2 Apr 2026
- 2025Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis17 Sep 2025
- 2025Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Traces29 Jan 2025
- 2024A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion20 Sep 2024
- 2024HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale9 Sep 2024
- 2024CodeR: Issue Resolving with Multi-Agent and Task Graphs3 Jun 2024
- 2023Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems5 May 2023
- 2022Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT527 Nov 2022
- 2022Infrared: A Meta Bug Detector18 Sep 2022
- 2022BigIssue: A Realistic Bug Localization Benchmark21 Jul 2022
- 2022DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs4 May 2022
1 more
- 2024HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scalecited by 61
- 2025Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysiscited by 33
- 2024A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexioncited by 8
- 2025Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis17 Sep 2025
- 2024A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion20 Sep 2024
- 2024HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale9 Sep 2024
- 2024CodeR: Issue Resolving with Multi-Agent and Task Graphscited by 89
- 2021Black-Box Testing of Deep Neural Networks Through Test Case Diversitycited by 18
- 2022DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programscited by 16
- 2022Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5cited by 9
- 2023Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systemscited by 1
- 2025Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Tracescited by 1
- 2022BigIssue: A Realistic Bug Localization Benchmarkcited by 0
- 2022Infrared: A Meta Bug Detectorcited by 0
- 2026Improving MPI Error Detection and Repair with Large Language Models and Bug Referencescited by 0
- 2026From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learningcited by 0
- 2026From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning29 Sep 2026
- 2026Improving MPI Error Detection and Repair with Large Language Models and Bug References2 Apr 2026
- 2025Fault Localization via Fine-tuning Large Language Models with Mutation Generated Stack Traces29 Jan 2025
- 2024CodeR: Issue Resolving with Multi-Agent and Task Graphs3 Jun 2024
- 2023Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems5 May 2023
- 2022Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT527 Nov 2022
- 2022Infrared: A Meta Bug Detector18 Sep 2022
- 2022BigIssue: A Realistic Bug Localization Benchmark21 Jul 2022
- 2022DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs4 May 2022
- 2021Black-Box Testing of Deep Neural Networks Through Test Case Diversity20 Dec 2021