Abstract
Generation of test data for path coverage is an important issue of software testing, but previous methods are only suitable for the case that a program only has a small number of paths. We focus on the problem of generating test data for many paths coverage in this paper, and present a method of evolutionary generation of test data for many paths coverage. First, target paths are divided into several groups based on their similarity, 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. Our method is applied in 2 benchmark programs, and compared with some previous methods. The experimental results show that our method has advantage in time-consumption and the number of uncovered target paths. Our achievement provides an efficient way for generating test data of complicated software.
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