An Empirical Study of Pairwise Test Set Generation Using a Genetic Algorithm

Pairwise test set generation is the process of producing a subset of all possible test case inputs to a system in situations where exhaustive testing is not possible or is prohibitively expensive. For a given system under test with a set of input parameters where each parameter can take on one of a discrete set of values, a pairwise test set consists of a collection of vectors which capture all possible combinations of pairs of parameter values. Generating pairwise test sets with a minimal size has been shown to be an NP-complete problem, and several deterministic generation algorithms have been published. This paper describes the results of an investigation of pairwise test set generation using a genetic algorithm. The genetic algorithm approach produced pairwise test sets with comparable or smaller (better) size compared with published results for deterministic algorithms for 39 out of 40 benchmark problems. However, the genetic algorithm test set generation technique required significantly longer processing time in all cases. The results illustrate that generation of pairwise test sets using a genetic algorithm is possible, and suggest that the technique may be both practical and useful in certain software testing situations.