Causal Testing: Finding Defects' Root Causes
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Abstract (arXiv)
Understanding the root cause of a defect is critical to isolating and repairing buggy behavior. We present Causal Testing, a new method of root-cause analysis that relies on the theory of counterfactual causality to identify a set of executions that likely hold key causal information necessary to understand and repair buggy behavior. Using the Defects4J benchmark, we find that Causal Testing could be applied to 71% of real-world defects, and for 77% of those, it can help developers identify the root cause of the defect. A controlled experiment with 37 developers shows that Causal Testing improves participants' ability to identify the cause of the defect from 80% of the time with standard testing tools to 86% of the time with Causal Testing. The participants report that Causal Testing provides useful information they cannot get using tools such as JUnit. Holmes, our prototype, open-source Eclipse plugin implementation of Causal Testing, is available at http://holmes.cs.umass.edu/.
Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2018.
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Citing papers per year: 2022: 2; 2023: 8; 2024: 4; 2025: 3; 2026: 5.
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- 2022Causality-based Neural Network Repaircited by 44
- 2024Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verificationcited by 25
- 2023Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approachcited by 15
- 2022Unicorn: Reasoning about Configurable System Performance through the lens of Causalitycited by 12
- 2023Causality Analysis for Evaluating the Security of Large Language Modelscited by 9
- 2023Causality-Aided Trade-off Analysis for Machine Learning Fairnesscited by 6
- 2023CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robotscited by 4
- 2024QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learningcited by 3
- 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
- 2023Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)cited by 2
- 2024CURE: Simulation-Augmented Auto-Tuning in Roboticscited by 2
- 2023Reasoning-Based Software Testingcited by 1
10 more
- 2023Causal fault localisation in dataflow systemscited by 1
- 2023Detection and Evaluation of bias-inducing Features in Machine learningcited by 1
- 2026CAM: A Causality-based Analysis Framework for Multi-Agent Code Generation Systemscited by 1
- 2026Towards Counterfactual Explanation and Assertion Inference for CPS Debuggingcited by 1
- 2025Causality-Driven Neural Network Repair: Challenges and Opportunitiescited by 0
- 2025Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Modelscited by 0
- 2025SoK: a Comprehensive Causality Analysis Framework for Large Language Model Securitycited by 0
- 2026Causal Software Engineering: A Vision and Roadmapcited by 0
- 2026Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineeringcited by 0
- 2026SWE-Game: Can Coding Agents Build the Games We Want?cited by 0
- 2026SWE-Game: Can Coding Agents Build the Games We Want?27 Sep 2026
- 2026Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering26 Jun 2026
- 2026Causal Software Engineering: A Vision and Roadmap4 May 2026
- 2026Towards Counterfactual Explanation and Assertion Inference for CPS Debugging9 Apr 2026
- 2026CAM: A Causality-based Analysis Framework for Multi-Agent Code Generation Systems2 Feb 2026
- 2025SoK: a Comprehensive Causality Analysis Framework for Large Language Model Security4 Dec 2025
- 2025Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models31 Jul 2025
- 2025Causality-Driven Neural Network Repair: Challenges and Opportunities24 Apr 2025
- 2024Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification18 Dec 2024
- 2024Causal Reasoning in Software Quality Assurance: A Systematic Review30 Aug 2024
- 2024QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learning17 Aug 2024
- 2024CURE: Simulation-Augmented Auto-Tuning in Robotics8 Feb 2024
10 more
- 2023Causality Analysis for Evaluating the Security of Large Language Models13 Dec 2023
- 2023Detection and Evaluation of bias-inducing Features in Machine learning19 Oct 2023
- 2023Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach10 Oct 2023
- 2023Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)11 Sep 2023
- 2023Causality-Aided Trade-off Analysis for Machine Learning Fairness22 May 2023
- 2023Causal fault localisation in dataflow systems24 Apr 2023
- 2023Reasoning-Based Software Testing2 Mar 2023
- 2023CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots18 Jan 2023
- 2022Causality-based Neural Network Repair20 Apr 2022
- 2022Unicorn: Reasoning about Configurable System Performance through the lens of Causality20 Jan 2022
- 2023Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approachcited by 15
- 2022Unicorn: Reasoning about Configurable System Performance through the lens of Causalitycited by 12
- 2023Causality-Aided Trade-off Analysis for Machine Learning Fairnesscited by 6
- 2023CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robotscited by 4
- 2024QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learningcited by 3
- 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
- 2023Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)cited by 2
- 2024CURE: Simulation-Augmented Auto-Tuning in Roboticscited by 2
- 2023Reasoning-Based Software Testingcited by 1
- 2023Causal fault localisation in dataflow systemscited by 1
- 2023Detection and Evaluation of bias-inducing Features in Machine learningcited by 1
- 2026Towards Counterfactual Explanation and Assertion Inference for CPS Debuggingcited by 1
12 most cited of 15 All 15 (JSON)
- 2026Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering26 Jun 2026
- 2026Causal Software Engineering: A Vision and Roadmap4 May 2026
- 2026Towards Counterfactual Explanation and Assertion Inference for CPS Debugging9 Apr 2026
- 2025Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models31 Jul 2025
- 2024Causal Reasoning in Software Quality Assurance: A Systematic Review30 Aug 2024
- 2024QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learning17 Aug 2024
- 2024CURE: Simulation-Augmented Auto-Tuning in Robotics8 Feb 2024
- 2023Detection and Evaluation of bias-inducing Features in Machine learning19 Oct 2023
- 2023Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach10 Oct 2023
- 2023Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)11 Sep 2023
- 2023Causality-Aided Trade-off Analysis for Machine Learning Fairness22 May 2023
- 2023Causal fault localisation in dataflow systems24 Apr 2023
12 newest of 15 All 15 (JSON)