A Novel Mutation Operator for Search-Based Test Case Selection

Test case selection has been a widely investigated technique to increase the cost-effectiveness of software testing. Because the search space in this problem is huge, search-based approaches have been found effective, where an optimization algorithm (e.g., a genetic algorithm) applies mutation and crossover operators guided by corresponding objective functions with the goal of reducing the test execution cost while maintaining the overall test quality. The de-facto mutation operator is the bit-flip mutation, where a test case is mutated with a probability of 1/N, N being the total number of test cases in the original test suite. This has a core disadvantage: an effective test case and an ineffective one have the same probability of being selected or removed. In this paper, we advocate for a novel mutation operator that promotes selecting effective test cases while removing the ineffective ones. To this end, instead of applying a probability of 1/N to every single test case in the original test suite, we calculate new selection and removal probabilities. This is carried out based on the adequacy criterion of each test case, determined before executing the algorithm (e.g., based on historical data). We integrate our approach in the domain of Cyber-Physical Systems (CPSs) within a widely applied dataset. Our results suggests that the proposed mutation operator can increase the effectiveness of search-based test case selection methods, especially when the time budget for executing test cases is low.