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This paper reviews existing work in software engineering that applies statistical causal inference methods.
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G. K. Baah, A. Podgurski, M. J. Harrold, Matching Test Cases for Effective Fault Localization , Technical Report, Georgia Institute of Technology, accepted: 2012-12-05T00:31:00Z (2011). URL https://smartech.gatech.edu/handle/1853/45503
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G. Shu, B. Sun, A. Podgurski, F. Cao, MFL: Method-level fault localization with causal inference , in: Proceedings - IEEE 6th International Conference on Software Testing, Verification and Validation, ICST 2013, 2013, pp. 124–133 · 2013
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M. Peralta, S. Mukhopadhyay, Code-change impact analysis using counterfactuals: Theory and implementation , International Journal of Software Engineering and Knowledge Engineering 23 (10) (2013) 1459–1486 · 2013
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L. Li, J. Liu, Z. Zhou, H. Luo, W. Liu, J. Li, Causal inference based service dependency graph for statistical service fault localization , in: Proceedings - 2014 10th International Conference on Semantics, Knowledge and Grids, SKG 2014, 2014, pp. 41–48 · 2014
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Z. Bai, G. Shu, A. Podgurski, NUMFL: Localizing faults in numerical software using a value-based causal model , in: 2015 IEEE 8th International Conference on Software Testing, Verification and Validation, ICST 2015 - Proceedings, 2015 · 2015
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X. Wang, S. Jiang, X. Ju, H. Cao, Y. Liu, Mitigating the Dependence Confounding Effect for Effective Predicate-Based Statistical Fault Localization, in: 2015 IEEE 39th Annual Computer Software and Applications Conference, Vol. 2, 2015, pp. 105–114, iSSN: 0730-3157 · 2015
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S.-F. Sun, A. Podgurski, Properties of Effective Metrics for Coverage-Based Statistical Fault Localization, in: 2016 IEEE International Conference on Software Testing, Verification and Validation (ICST), 2016, pp. 124–134 · 2016
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V. Musco, M. Monperrus, P. Preux, Mutation-Based Graph Inference for Fault Localization, in: 2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM), 2016, pp. 97–106, iSSN: 2470-6892 · 2016
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