A Hybrid Artificial Bee Colony Strategy for t-way Test Set Generation with Constraints Support

Abstract

t-way interaction testing is a systematic approach for exhaustive test set generation. It is a vital test planning method in software testing, which generates test sets based on interaction between parameters to cover every possible test sets combinations. t-way strategy clarifies the interaction strength between the number of parameters. However, there are some test sets combinations that should be excluded when generating the final test set as a result of invalid outputs, impossible or unwanted test sets combinations (e.g. system requirements set). These types of set combinations are known as constraint’s combinations or forbidden combinations. From existing studies, several t-way strategies have been proposed to address the test set combination problem, however, generating the optimal test set is still open research being an NP-hard problem. Therefore, this study proposed a novel hybrid artificial bee colony (HABC) t-way test set generation strategy with constraints support. The proposed approach is based on a hybrid artificial bee colony (ABC) algorithm with a particle swarm optimization (PSO) algorithm. PSO was integrated as the exploratory agent for the ABC hence the hybrid nature. The information sharing ability of PSO via the Weight Factor is used to enhance the performance of ABC. The output of the hybrid ABC is a set of promising optimal test set combinations. The results of the experiments showed that HABC outperformed and yielded better test sets than existing methods (HSS, LAHC, SA_SAT, PICT, TestCover, mATEG_SAT).