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An investigation of the applicability of design of experiments to software testing

Kuhn, D.R., Reilly, M.J.·2004·27th Annual NASA Goddard/IEEE Software Engineering Workshop, 2002. Proceedings.

Abstract

Approaches to software testing based on methods from the field of design of experiments have been advocated as a means of providing high coverage at relatively low cost. Tools to generate all pairs, or higher n-degree combinations, of input values have been developed and demonstrated in a few applications, but little empirical evidence is available to aid developers in evaluating the effectiveness of these tools for particular problems. We investigate error reports from two large open-source software projects, a browser and Web server, to provide preliminary answers to three questions: Is there a point of diminishing returns at which generating all n-degree combinations is nearly as effective as all n+1-degree combinations? What is the appropriate value of n for particular classes of software? Does this value differ for different types of software, and by how much? Our findings suggest that more than 95% of errors in the software studied would be detected by test cases that cover all 4-way combinations of values, and that the browser and server software were similar in the percentage of errors detectable by combinations of degree 2 through 6.

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Metadata

Authors
Kuhn, D. R., & Reilly, M. J.
Venue
27th Annual NASA Goddard/IEEE Software Engineering Workshop, 2002. Proceedings.
Published
2004-03-23
Pages
91-95
OpenAlex
W2544700366
Cited by
296
References
0
FWCI
9.16
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: An investigation of the applicability of design of experiments to software testing. https://app.cochat.ai/papers/10.1109/sew.2002.1199454
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Kuhn, D. R., & Reilly, M. J. (2004). An investigation of the applicability of design of experiments to software testing. 27th Annual NASA Goddard/IEEE Software Engineering Workshop, 2002. Proceedings. https://doi.org/10.1109/sew.2002.1199454

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.