Automated test data generation based on particle swarm optimisation with convergence speed controller. Issue 2 (26th April 2019)

• Record Type: Journal Article
• Authors: Liu, Fangqing; Huang, Han; Li, Xueqiang; Hao, Zhifeng
• Is Part Of: CAAI transactions on intelligence technology. Volume 2:Issue 2(2017)
• Page Start: 73
• Page End: 79
• Publication Date: 2019-04-26
• Subjects: particle swarm optimisation -- search problems -- program testing; software testing -- ATDG‐PC -- high‐rate path coverage -- optimisation problem -- search scale -- rapid meta‐heuristic algorithm particle -- large‐scale search -- convergence speed controller -- convergence speed balance -- test cases -- uncovered paths -- classic Hadoop programmes -- meta‐heuristic algorithms -- automated test data generation -- particle swarm optimisation; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2017.0004
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Automated test data generation for path coverage (ATDG‐PC) plays an important role in software testing. In this study, ATDG‐PC is applied to the case of cloud computing such as Hadoop programmes which are more difficult to search for high‐rate path coverage than the normal programmes. The search scale of ATDG‐PC is usually enormous, while the relationship between the variables and the paths is unknown. First, a rapid meta‐heuristic algorithm particle swarm optimisation (PSO) was chosen to solve the problem of large‐scale search. Second, the strategy of convergence speed controller was used to improve the performance of PSO by mining heuristic information from the found paths. The controller adjusts the convergence speed balance periodically by two conditions and rules. The first strategy slows the convergence speed when the algorithm is premature convergence and is trapped in a local optimum. The second strategy accelerates the convergence speed if the algorithm does not converge after many iterations. The effectiveness of the proposed algorithm is evaluated by classic Hadoop programmes of cloud computing. The experimental results indicate that the proposed algorithm can reduce a great number of test cases for path coverage, compared with other metaheuristic algorithms for automated test data generation.