On Test Case Prioritization Using Ant Colony Optimization Algorithm

Test case prioritization technology improves the efficiency of software testing by optimizing the execution order of test cases, which is an important research topic of software regression testing. In order to solve the problem of requirement-based test case prioritization, this paper proposed a solution based on ant colony optimization algorithm and gave its two different implementation methods: distance-based and index-based implementation. Firstly, a general indicator based on requirements was designed to evaluate the test cases. Secondly, the concept of test case attractivity was proposed, and the definition of the distance between test cases was given based on it. Finally, the main design strategies such as the pheromone update strategy, the optimal solution update strategy, and the local optimal mutation strategy were given. The experimental results show that the method has good global optimization ability, and its overall effect is better than particle swarm optimization algorithm, genetic algorithm and random testing.