HABC: Hybrid artificial bee colony for generating variable T-way test sets

Exhaustive testing of occurred interaction amongst components (i.e., parameters and values) of a software system is usually impossible due to some factors such as the restriction of budget and time. One of the effective software testing techniques used for detecting faults of interactions between components is combinatorial testing (CT). CT is a black box testing technique, used to find the mistakes among components of a software system in a systematic and effective way. However, CT is highly complex (NP-hard). The input variables for a realworld software may diverge in how they strongly influence variable strength (VS) interaction can achieve that effectively. This paper proposed a hybrid artificial bee colony (HABC) strategy based on the hybrid artificial bee colony algorithm and practical swarm optimization to generate optimal test suite of variable strength interaction. PSO was integrated as the exploitation 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 HABC is a set of promising optimal test set combinations. Through several benchmark experiments, HABC proved the effectiveness of the proposed strategy. The HABC has achieved 76.31 better result than most of the compared strategies. © 2020 School of Engineering, Taylor's University.