Evolutionary algorithm with convergence speed controller for automated software test data generation problem

Abstract

Software testing is an important process of software development. One of the challenges in testing software is to generate test cases which help to reveal errors. Automated software test data generation problem is hard because it needs to search the whole feasible area to find test cases covering all possible paths under acceptable time consumption. In this paper, evolutionary algorithm with convergence speed controller (EA-CSC) is presented for using the least test case overhead in solving automated test case generation problem. EA-CSC is designed as a framework which have fast convergence speed and capability to jump out of the local optimal solution over a range of problems. There are two critical steps in EA-CSC. The adaptive step size searching method accelerates the convergence speed of EA. The mutation operator can disrupt the population distribution and slows down the convergence process of EA. Moreover, the EA-CSC results are compared to the algorithms tested on the same benchmark problems, showing strong competitive.