The coupling effect: fact or fiction
TAV3 Proceedings of the ACM SIGSOFT '89 third symposium on Software testing, analysis, and verification
A Theory of Fault-Based Testing
IEEE Transactions on Software Engineering
PIE: A Dynamic Failure-Based Technique
IEEE Transactions on Software Engineering
Dynamic impact analysis: a cost-effective technique to enforce error-propagation
ISSTA '93 Proceedings of the 1993 ACM SIGSOFT international symposium on Software testing and analysis
Semantic metrics through error flow analysis
Journal of Systems and Software - Special issue on the Oregon Metric Workshop
An experimental approach to analyzing software semantics using error flow information
ISSTA '94 Proceedings of the 1994 ACM SIGSOFT international symposium on Software testing and analysis
Using perturbation analysis to measure variation in the information content of test sets
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Software error analysis: a real case study involving real faults and mutations
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
A semantic model of program faults
ISSTA '96 Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis
Software Testability: The New Verification
IEEE Software
A Perturbation-based Testing Strategy
ICECCS '02 Proceedings of the Eighth International Conference on Engineering of Complex Computer Systems
A theory of error-based testing
A theory of error-based testing
Error flow in computer programs
Error flow in computer programs
Data contamination and row-level error identification
ICAI'09 Proceedings of the 10th WSEAS international conference on Automation & information
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Error flow analysis and testing techniques focus on the introduction of errors through code faults into data states of an executing program, and their subsequent cancellation or propagation to output. The goals and limitations of several error flow techniques are discussed, including mutation analysis, fault-based testing, PIE analysis, and dynamic impact analysis. The attributes desired of a good error flow technique are proposed, and a model called dynamic error flow analysis (DEFA) is described that embodies many of these attributes. A testing strategy is proposed that uses DEFA information to select an optimal set of test paths and to quantify the results of successful testing. An experiment is presented that illustrates this testing strategy. In this experiment, the proposed testing strategy outperforms mutation testing in catching arbitrary data state errors.