mlsanityData to 7 Oct 2026

Causal Testing: Finding Defects' Root Causes

arXiv 1809.06991 ↗First posted 19 Sep 2018cs.SEcited by 22

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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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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 8; 2024: 4; 2025: 3; 2026: 5.

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  1. 2022Causality-based Neural Network Repaircited by 44
  2. 2024Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verificationcited by 25
  3. 2023Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approachcited by 15
  4. 2022Unicorn: Reasoning about Configurable System Performance through the lens of Causalitycited by 12
  5. 2023Causality Analysis for Evaluating the Security of Large Language Modelscited by 9
  6. 2023Causality-Aided Trade-off Analysis for Machine Learning Fairnesscited by 6
  7. 2023CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robotscited by 4
  8. 2024QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learningcited by 3
  9. 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
  10. 2023Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning (Extended Version)cited by 2
  11. 2024CURE: Simulation-Augmented Auto-Tuning in Roboticscited by 2
  12. 2023Reasoning-Based Software Testingcited by 1
10 more
  1. 2023Causal fault localisation in dataflow systemscited by 1
  2. 2023Detection and Evaluation of bias-inducing Features in Machine learningcited by 1
  3. 2026CAM: A Causality-based Analysis Framework for Multi-Agent Code Generation Systemscited by 1
  4. 2026Towards Counterfactual Explanation and Assertion Inference for CPS Debuggingcited by 1
  5. 2025Causality-Driven Neural Network Repair: Challenges and Opportunitiescited by 0
  6. 2025Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Modelscited by 0
  7. 2025SoK: a Comprehensive Causality Analysis Framework for Large Language Model Securitycited by 0
  8. 2026Causal Software Engineering: A Vision and Roadmapcited by 0
  9. 2026Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineeringcited by 0
  10. 2026SWE-Game: Can Coding Agents Build the Games We Want?cited by 0