Test Suite Optimization using Mutated Artificial Bee Colony

Software test suite optimization is one of the most important issue in software testing as testing consumes a lot of time in executing redundant test cases. In this paper, we have proposed and implemented a new approach for test suite optimization, namely, Mutated Artificial Bee Colony. Artificial Bee colony algorithm combines local search carried out by employed and onlooker bees with global search managed by scouts and gives optimal results. To further improve the global search capabilities of ABC algorithm, Mutation function of Genetic Algorithm is permuted with Onlooker bee and Scout bee, giving four different approaches for test suite optimization that are, Simple ABC, ABC with mutation at onlooker bee, ABC with mutation at Scout bee and ABC with mutation at both onlooker bee and scout bee. All approaches will be compared on the basis of runtime and number of iterations. Based on the experimental results, it has been verified and validates the proposed algorithm. The proposed algorithm would be beneficial for the software testers for selecting a minimal test suite for testing.