Selecting Software Test Data Using Data Flow Information
IEEE Transactions on Software Engineering
The program dependence graph and its use in optimization
ACM Transactions on Programming Languages and Systems (TOPLAS)
PLDI '90 Proceedings of the ACM SIGPLAN 1990 conference on Programming language design and implementation
IEEE Transactions on Software Engineering
An Analysis of Test Data Selection Criteria Using the RELAY Model of Fault Detection
IEEE Transactions on Software Engineering
Critical slicing for software fault localization
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Visualization of test information to assist fault localization
Proceedings of the 24th International Conference on Software Engineering
Isolating cause-effect chains from computer programs
Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering
CIL: Intermediate Language and Tools for Analysis and Transformation of C Programs
CC '02 Proceedings of the 11th International Conference on Compiler Construction
ICSE '81 Proceedings of the 5th international conference on Software engineering
Locating causes of program failures
Proceedings of the 27th international conference on Software engineering
Scalable statistical bug isolation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
SOBER: statistical model-based bug localization
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Empirical Software Engineering
Pruning dynamic slices with confidence
Proceedings of the 2006 ACM SIGPLAN conference on Programming language design and implementation
On the Accuracy of Spectrum-based Fault Localization
TAICPART-MUTATION '07 Proceedings of the Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION
Digraphs: Theory, Algorithms and Applications
Digraphs: Theory, Algorithms and Applications
Lightweight fault-localization using multiple coverage types
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
PADS '09 Proceedings of the 2009 ACM/IEEE/SCS 23rd Workshop on Principles of Advanced and Distributed Simulation
Capturing propagation of infected program states
Proceedings of the the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Directed test generation for effective fault localization
Proceedings of the 19th international symposium on Software testing and analysis
Causal inference for statistical fault localization
Proceedings of the 19th international symposium on Software testing and analysis
Bioinformatics
Comprehensive evaluation of association measures for fault localization
ICSM '10 Proceedings of the 2010 IEEE International Conference on Software Maintenance
Modifying test suite composition to enable effective predicate-level statistical debugging
NFM'12 Proceedings of the 4th international conference on NASA Formal Methods
Reducing confounding bias in predicate-level statistical debugging metrics
Proceedings of the 34th International Conference on Software Engineering
Diversity maximization speedup for fault localization
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
F3: fault localization for field failures
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Comparative causality: explaining the differences between executions
Proceedings of the 2013 International Conference on Software Engineering
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Dynamic program dependences are recognized as important factors in software debugging because they contribute to triggering the effects of faults and propagating the effects to a program's output. The effects of dynamic dependences also produce significant confounding bias when statistically estimating the causal effect of a statement on the occurrence of program failures, which leads to poor fault localization results. This paper presents a novel causal-inference technique for fault localization that accounts for the effects of dynamic data and control dependences and thus, significantly reduces confounding bias during fault localization. The technique employs a new dependence-based causal model together with matching of test executions based on their dynamic dependences. The paper also presents empirical results indicating that the new technique performs significantly better than existing statistical fault-localization techniques as well as our previous fault localization technique based on causal-inference methodology.