An Automatic Software Testing Method to Discover Hard-to-Detect Faults Using Hybrid Olympiad Optimization Algorithm

The enhancement of software system quality is achieved through a process called software testing, which is a time and cost-intensive stage of software development. As a result, automating software tests is recognized as an effective solution that can simplify time-consuming and arduous testing activities. Generating test data with maximum branch coverage and fault discovery capability is an NP-complete optimization problem. Various methods based on heuristics and evolutionary algorithms have been suggested to create test suites that provide the most feasible coverage. The main disadvantages of past approaches include inadequate branching coverage, fault detection rate, and unstable results. The main objectives of the current research are to improve the branch coverage rate, fault detection rate, success rate, and stability. This research has suggested an efficient technique to produce test data automatically utilizing a hybrid version of Olympiad Optimization Algorithms (OOA) in conjunction with genetic algorithm (GA) operators theory. Maximum coverage, fault detection capability, and success rate are the main characteristics of produced test data. Various experiments have been conducted on the nine standard benchmark programs. Regarding the results, the suggested method provides 99.92% average coverage, a success rate of 99.20%, an average generation of 5.76, and an average time of 7.97 s. Based on the fault injection experiment’s results, the proposed method can discover about 89% of the faults injected by mutation testing tools such as MuJava. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.