Improved evolutionary generation of test data for multiple paths in search-based software testing

Search-based software testing has achieved great attention recently, but the efficiency is still the bottleneck of it. This paper focuses on improving the efficiency of generating test data for multiple paths. Genetic algorithms are chosen as the heuristic algorithms in search-based software testing in this paper. First, we propose an improved grouping strategy of target paths to balance the load of each calculation resource. This work makes a contribution to the parallel execution in search-based software testing. Then, common constraints of the target paths in the same group are collected to reduce the search space of test data. Symbolic execution technique is used in this phase. Based on the reduced search space, we can accelerate the convergence of search process and improve the efficiency of search-based software testing. Finally, our method is applied to some study cases to compare with other methods.