Non-bio-inspired Metaheuristics in Software Testing: A Systematic Literature Review

Abstract

The software testing phase usually consumes a lot of the development of software projects time in order to find defects before release. Different strategies have been approached to optimize this phase of the testing stage. Metaheuristics are important in software testing due to their ability to find optimal or near-optimal solutions in complex situations. This research aims to analyze the current status of the application of metaheuristics that assist in software testing phase activities, specifically the most representative Non-bio-inspired algorithms (NBA) are surveyed, being Hill Climbing the most reported. The main activities of the software testing where NBA were implemented, were test case and test data generation and test case prioritization. It was concluded that NBAs used on their own are only viable in some activities of the software testing phase. As future work, it is proposed to investigate the use of hybrid algorithms and approaches in software testing phase.