Augmenting ant colony optimization with adaptive random testing to cover prime paths

Abstract

Test data generation has a notable impact on the performance of software testing. A well-known approach to automate this activity is search-based test data generation. Most studies in this area use branch coverage as the test criterion. Since the prime path coverage criterion includes branch coverage, it has higher probability to detect software failures than the branch coverage criterion. This paper customizes and improves ant colony optimization (ACO) to provide a test data generation approach for covering prime paths. The proposed approach incorporates the notion of input space partitioning to maintain pheromone values in the search space. In addition, it employs the idea of adaptive random testing in the local search. At last, it uses the information of program predicates in order to make a relation between the logic of the program and pheromone values in the search space. The experimental results confirm the positive effects of the mentioned contributions, especially for programs with complex predicates. Furthermore, they represent that, on average, test suites generated by the proposed approach has 9% better mutation score in comparison to test suites produced by EvoSuite, a well-known test data generation tool.