Abstract
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.
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