Algorithmic methods for covering arrays of higher index

Ryan E. Dougherty (), Kristoffer Kleine (), Michael Wagner (), Charles J. Colbourn () and Dimitris E. Simos ()
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Ryan E. Dougherty: United States Military Academy
Kristoffer Kleine: SBA Research
Michael Wagner: SBA Research
Charles J. Colbourn: Arizona State University
Dimitris E. Simos: SBA Research

Journal of Combinatorial Optimization, 2023, vol. 45, issue 1, No 21, 21 pages

Abstract: Abstract Covering arrays are combinatorial objects used in testing large-scale systems to increase confidence in their correctness. To do so, each interaction of at most a specified number t of factors is represented in at least one test; that is, the covering array has strength t and index 1. For certain systems, the outcome of running a test may be altered by variability of the interaction effect or by measurement error of the test result. To improve the efficacy of testing, one can ensure that each interaction of t or fewer factors is represented in at least $$\lambda $$ λ tests. When $$\lambda > 1$$ λ > 1 , this leads to covering arrays of higher index. We explore two algorithmic methods for constructing covering arrays of higher index. One is based on the in-parameter-order algorithm, and the other employs a conditional expectation paradigm. We compare these two by performing experiments on real-world benchmarks and on uniform parameter sets.

Keywords: Covering array; Conditional expectation; In-parameter-order algorithm; Software testing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10878-022-00947-x

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