Enhancing multi-objective test case selection through the mutation operator

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 cost-effective test cases while removing the ineffective and expensive 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 as well as the cost of each test case, determined before executing the algorithm (e.g., based on historical data). We evaluate our approach in 13 case study system, including 3 industrial case studies, in three different application domains (i.e., Cyber-Physical Systems (CPSs), continuous integration systems and industrial control systems). Our results suggests that the proposed approach can increase the cost-effectiveness of search-based test case selection methods, especially when the time budget for executing test cases is low.