On the Application of Quick Artificial Bee Colony Algorithm (qABC) for Attenuation of Test Suite in Real-Time Software Applications

Software testing plays a vital role during the software development process, as it ensures quality software deployment. Success of software testing depends on the design of effective test cases. To achieve the optimization of generated test cases, the proposed approach combines both global and local searches by means of intelligent agents which exhibit the behaviour of employed bees, onlooker bees, and scout bees in the qABC algorithm. The proposed qABC algorithm has key improvements over the basic artificial bee colony algorithm (ABC) in test optimization by reducing redundancy, filtering of test cases in each iteration and parallel working of the bees. Further, the fitness evaluation of the test cases is done by employing two test adequacy metrics namely path coverage and mutation score. Further, the experimental evaluation of qABC, GA, and the basic ABC based test cases is done using several case study applications. The result shows that qABC outperforms the other algorithms in terms of effectiveness of test cases in revealing the faults with less time and a smaller number of test cases.

Automated summary

Software testing is crucial for ensuring quality software deployment. Effective test case design is key to successful testing. The proposed approach combines global and local searches using intelligent agents inspired by bees, resulting in improved optimization of test cases. Additionally, the proposed algorithm enhances test optimization by reducing redundancy, filtering test cases, and enabling parallel working of bees. The fitness of test cases is evaluated using path coverage and mutation score, and experimental evaluations show that the proposed algorithm outperforms other algorithms in terms of effectiveness and efficiency.