Essent: An Arithmetic Optimization Algorithm with Enhanced Scatter Search Strategy for Automated Test Case Generation

• DOI: 10.2139/ssrn.4517470
• Publication Date: 2023-07-21T14:20:24Z
• Authors: Xiguang Li; Baolu Feng; Liang Zhao; Yunhe Sun
• Keywords: Optimization algorithm

As one of the main research tasks in software testing, automated test case generation based on path coverage (ATCG-PC) aims to achieve maximum with a minimized set cases. In ATCG-PC, correlation among dimensions cases is widely utilized academia minimize search efforts search-based algorithm. Nevertheless, information related target selection not utilized, which leads blind decision-making by algorithm during selection. Therefore, this paper proposes an enhanced scatter strategy using opposition-based learning. An arithmetic optimization also proposed solve ATCG-PC strategy, namely, ESSENT. The ESSENT selects lowest entropy uncovered paths as path, and generates new that cover modifying existing performance evaluated six iFogSim subprograms Stanford coreNLP subprograms. Experiment results show achieves higher convergence rate than other state-of-the-art algorithms. Furthermore, it enables fewer