Constrained multi-objective test data generation based on set evolution

A crucial task of software testing is the generation of high-quality test data, so as to find defects and errors during various periods of software development. However, existing coverage-based testing methods seldom consider the fault finding ability of the test data. This paper establishes a constrained multi-objective model of test data generation, so that the generated test suite has better spatial distribution on the basis of satisfying statement coverage criterion, and thereby enhance its error detection ability. In addition, the authors propose a genetic algorithm (GA) based on set evolution to solve the model. The experimental results show that the test data generated by the proposed model have higher fault finding ability than statement coverage testing and adaptive random testing; in addition, compared with conventional GAs, the proposed algorithm needs less execution time with the number of test data not increasing significantly.