Estimating t-way Fault Profile Evolution During Testing

Published

June 10, 2016

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

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/COMPSAC.2016.110

Local Download

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 October 27, 2025)