Test Set Generation for Pairwise Testing Using Genetic Algorithms
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초록
In software systems, it has been observed that a fault is often caused by an interaction between a small numberof input parameters. Even for moderately sized software systems, exhaustive testing is practically impossibleto achieve. This is either due to time or cost constraints. Combinatorial (t-way) testing provides a techniqueto select a subset of exhaustive test cases covering all of the t-way interactions, without much of a loss to thefault detection capability. In this paper, an approach is proposed to generate 2-way (pairwise) test sets usinggenetic algorithms. The performance of the algorithm is improved by creating an initial solution using theoverlap coefficient (a similarity matrix). Two mutation strategies have also been modified to improve theirefficiency. Furthermore, the mutation operator is improved by using a combination of three mutationstrategies. A comparative survey of the techniques to generate t-way test sets using genetic algorithms was alsoconducted. It has been shown experimentally that the proposed approach generates faster results by achievinghigher percentage coverage in a fewer number of generations. Additionally, the size of the mixed coveringarrays was reduced in one of the six benchmark problems examined.
Abstract
In software systems, it has been observed that a fault is often caused by an interaction between a small numberof input parameters. Even for moderately sized software systems, exhaustive testing is practically impossibleto achieve. This is either due to time or cost constraints. Combinatorial (t-way) testing provides a techniqueto select a subset of exhaustive test cases covering all of the t-way interactions, without much of a loss to thefault detection capability. In this paper, an approach is proposed to generate 2-way (pairwise) test sets usinggenetic algorithms. The performance of the algorithm is improved by creating an initial solution using theoverlap coefficient (a similarity matrix). Two mutation strategies have also been modified to improve theirefficiency. Furthermore, the mutation operator is improved by using a combination of three mutationstrategies. A comparative survey of the techniques to generate t-way test sets using genetic algorithms was alsoconducted. It has been shown experimentally that the proposed approach generates faster results by achievinghigher percentage coverage in a fewer number of generations. Additionally, the size of the mixed coveringarrays was reduced in one of the six benchmark problems examined.
참고문헌 (27)
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