HABCSm: A Hamming Based t-way Strategy Based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

TL;DR

This paper introduces HABCSm, a novel hybrid meta-heuristic strategy combining Artificial Bee Colony and Particle Swarm Optimization, using Hamming distance to efficiently generate minimal variable strength test sets.

Contribution

It proposes the first hybrid Artificial Bee Colony-based t-way testing strategy utilizing Hamming distance for improved test set generation.

Findings

HABCSm outperforms existing strategies in test set minimization.

The hybrid approach enhances exploration and solution quality.

Experimental results confirm superior performance of HABCSm.

Abstract

Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution.