Optimizing Cost Function in Imperialist Competitive Algorithm for Path Coverage Problem in Software Testing

The search-based optimization methods have been used for the software engineering activities such as software testing. In the field of software testing, search-based test data generation refers to the application of meta-heuristic optimization methods to generate the test data that cover the code space of a program. Automatic test data generation that can cover all the software paths is known as a major challenge. This paper establishes a new cost function for automatic test data generation, which can traverse the non-iterative paths of the software control flow graphs (CFGs). This function is later compared with similar cost functions proposed in the other articles. The results obtained indicate the superior performance of the proposed function. Another innovation proposed in this paper is the application of the Imperialist Competitive Algorithm (ICA) in automatic test data generation along with the proposed cost function. Automatic test data generation is implemented through ICA as well as the genetic algorithm and particle swarm optimization algorithm for three software programs with different search space sizes. These algorithms are compared with each other in terms of the convergence speed, computational time, and local search. The test data generated by the proposed method achieved better results than the other algorithms in finding the number of non-iterative paths, convergence speed, and computational time with growing the searching space of the software CFG