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Estimating t-way Fault Profile Evolution During Testing

Published

Author(s)

Raghu N. Kacker, David R. Kuhn

Abstract

Empirical studies have shown that most software interaction faults involve one or two variables interacting, with progressively fewer triggered by three or more, and no failure has been reported involving more than six variables interacting. This paper introduces a model for the origin of this distribution. We start with two empirically reasonable assumptions regarding the distribution of branch conditions in code and the proportion of t-way combinations seen in testing, and show that the model closely reproduces empirical data on t-way fault distributions. Although the model was developed to explain the distribution of faults by t-way interaction strength, it is shown to reproduce the basic exponential reliability model as a special case at each level of interaction, t. The paper evaluates model predictions against empirical data, and discusses implications for detection and removal of interaction faults.
Proceedings Title
Proceedings of IEEE International Conference on Software Testing, Verification and Validation
ICST 2017
Conference Dates
March 13-18, 2017
Conference Location
Tokyo
Conference Title
IEEE International Conference on Software Testing, Verification and Validation ICST 2017

Keywords

Combinatorial testing, Software fault, Testing

Citation

Kacker, R. and Kuhn, D. (2016), Estimating t-way Fault Profile Evolution During Testing, Proceedings of IEEE International Conference on Software Testing, Verification and Validation ICST 2017, Tokyo, -1, [online], https://doi.org/10.1109/COMPSAC.2016.110 (Accessed April 23, 2024)
Created June 9, 2016, Updated September 18, 2020