Automated test case generation for path coverage by using grey prediction evolution algorithm with improved scatter search strategy

Abstract

Automated test case generation for path coverage (ATCG-PC), as an important task in software testing, aims to achieve the highest path coverage of a tested program by using as little computational overhead as possible. In ATCG-PC, “similar paths are usually executed by similar test cases” is a problem-specific knowledge which was touched by a handful of researchers but still underutilized. Inspired by the problem-specific knowledge, this paper designs a local search strategy by improving a scatter search strategy, and then proposes a grey prediction evolution algorithm with the improved scatter search strategy for ATCG-PC. Here, the improved scatter search strategy could obtain two feasible test cases by exploiting a dimension of a test case covering a certain path. The proposed algorithm is constructed by importing the improved scatter search strategy to the end of the reproduction operation of the grey prediction evolution algorithm holding strong exploration ability. Grey prediction evolution algorithm is first applied to solve ATCG-PC. The performance of the proposed algorithm is evaluated on six fog computing benchmark programs and six natural language processing benchmark programs. The experimental results demonstrate that the proposed algorithm can achieve the highest path coverage with the fewer test cases and running time than some state-of-the-art algorithms.