Evolutionary generation of test data for many paths coverage based on grouping

Path-oriented test data generation is an important issue of software testing, but the efficiency of existing methods needs to be further improved. We focus on the problem of generating test data for many paths coverage, and present a method of evolutionary generation of test data for many paths coverage based on grouping. First, target paths are divided into several groups according to their similarities, and each group forms a sub-optimization problem, which transforms a complicated optimization problem into several simpler sub-optimization problems; then a domain-based fitness is designed when genetic algorithms are employed to solve these problems; finally, these sub-optimization problems are simplified along with the process of generating test data, hence improving the efficiency of generating test data. Having analyzed the performance of our method theoretically, we apply it in some typical programs under test, and compare it with some previous methods. The experimental results show that our method has advantage in the number of evaluations and uncovered target paths.

• The first study to consider the problem of generating test data for many target paths.
• The first study to establish the optimization model for the problem of generating test data for many paths coverage.
• Propose a strategy of grouping target paths in order to reduce the difficulty of the problem.
• Propose a strategy of reconstructing sub-optimization problems during the evolution to reduce the difficulty further.