Gravitational search algorithm based strategy for combinatorial t-way test suite generation

Due to different users' requirements, contemporary software has become feature-rich in terms of input functions (i.e., parameters) and selections (i.e., values). Exhaustive testing on sophisticated software systems is impractical as far as testing time and cost are concerned. Various test case design strategies have been proposed in the literature, such as equivalence class partitioning, boundary value analysis and decision tables. Unlike earlier works, combinatorial t-way testing supports the detection of faults caused by two or more input parameter interactions and thus efficiently minimizes the size of the test suite. Over the past few years, metaheuristic algorithms have appeared to be the most common choice for researchers since their effectiveness proves to offer optimal/near-optimal results. However, generating a t-way test suite is an NP-hard problem, and no single t-way strategy can guarantee to show superiority to others for all types of system configurations. Hence, this paper presents a new t-way strategy based on the Gravitational Search Algorithm (GSA), known as the Gravitational Search Test Generator (GSTG). The primary contribution of this paper is that GSA has adapted for the first time to t-way test data generation. The benchmarking results showcase that GSTG obtains competitive results in most system configurations compared to other existing strategies and addresses higher combination coverage (i.e., t <= 10). (C) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

Automated summary

This paper presents a new t-way strategy based on the Gravitational Search Algorithm (GSA), known as the Gravitational Search Test Generator (GSTG), which demonstrates competitive results in most system configurations and achieves higher combination coverage.