Multi-Objective Heuristic Information Optimization Algorithm for Path Coverage-Oriented Test Data Generation

Abstract

Software testing is an important means to guarantee the quality of software products. The path coverage-oriented test data generation problem can be transformed into an optimization problem that can be solved by intelligent algorithms. To improve the efficiency of path coverage-oriented test data generation, an optimized algorithm which uses solution-based heuristic information is designed in view of the characteristics of the problem. Since there is usually a correspondence based on a certain branch node(s) between the test data of adjacent paths, we can use the test data information of the branch path that has been solved–regard it as the heuristic information of the algorithm–to generate the corresponding test data of the branch path that is difficult to meet. Therefore, the efficiency of problem solving can be increased by using heuristic information provided by the obtained test data. Based on the easy-to-cover path information in the key point path representation method, this paper designs an improved framework for accessing heuristic information, and adopts a multi-objective selection strategy, therefore designs a multi-objective heuristic information optimization algorithm for path coverage-oriented test data generation. Simulation experiments show that the algorithm can generate test data faster.