Microscale search-based algorithm based on time-space transfer for automated test case generation

Abstract

Automated test case generation for path coverage (ATCG-PC) is a major challenge in search-based software engineering due to its complexity as a large-scale black-box optimization problem. However, existing search-based approaches often fail to achieve high path coverage in large-scale unit programs. This is due to their expansive decision space and the presence of hundreds of feasible paths. In this paper, we present a microscale (small-size subsets of the decomposed decision set) search-based algorithm with time-space transfer (MISA-TST). This algorithm aims to identify more accurate subspaces consisting of optimal solutions based on two strategies. The dimension partition strategy employs a relationship matrix to track subspaces corresponding to the target paths. Additionally, the specific value strategy allows MISA-TST to focus the search on the neighborhood of specific dimension values rather than the entire dimension space. Experiments conducted on nine normal-scale and six large-scale benchmarks demonstrate the effectiveness of MISA-TST. The large-scale unit programs encompass hundreds of feasible paths or more than 1.00E+50 test cases. The results show that MISA-TST achieves significantly higher path coverage than other state-of-the-art algorithms in most benchmarks. Furthermore, the combination of the two time-space transfer strategies significantly enhances the performance of search-based algorithms like MISA, especially in large-scale unit programs.