Using arti .cial lifetechniques to generatetest cases forcombinatorial testing

TLDR

Combinatorial testing requires covering all t‑way combinations of input parameters, motivated by the fact that many faults arise from interactions among a few parameters. The study proposes new test generation algorithms for combinatorial testing using a genetic algorithm and an ant colony algorithm. The algorithms employ genetic and ant colony techniques to generate test suites that cover all required t‑way combinations. Experiments demonstrate the algorithms’ effectiveness, with particularly impressive results for t = 3.

Abstract

Combinatorial testing is a specification-based testing criterion, which requires that for each t-way combination of input parameters of a system, every combination of valid values of these t parameters be covered by at least one test case. This approach is motivated by the observation that in many applications a significant number of faults are caused by interactions of a smaller number of parameters. We propose new test generation algorithms for combinatorial testing based on two artificial life techniques: a genetic algorithm (GA) and an ant colony algorithm (ACA). The usefulness of these algorithms is demonstrated through experiments. In the case t = 3 in particular, our algorithms exhibited impressive results.