Influencing Factors' Analysis for the Performance of Parallel Evolutionary Test Case Generation for Web Applications

ABSTRACT Evolutionary test case generation plays a vital role in ensuring software quality and reliability. Since Web applications involve a large number of interactions between client and server, the dynamic evolutionary test case generation is very time‐consuming, which makes it difficult to apply in actual projects. Obviously, parallelization provides a feasible way to improve the efficiency and effectiveness of evolutionary test generation. In our previous research, the idea of parallelism has been introduced into the evolutionary test generation for Web applications. However, its performance is affected by many factors, such as migration scale, migration frequency, the number of browser processes and subpopulations, and so on. The analysis of influencing factors can guide enhancing the performance of evolutionary test generation. For this reason, this paper analyzes the factors that influence parallel evolutionary algorithms and how they affect the performance of test generation for Web applications. At the same time, different parallel evolutionary test generation methods are designed and implemented. Experiments are conducted on open‐source Web applications to generate test cases that meet the server‐side sensitive paths coverage criterion, providing guidance and suggestions for the parameter setting of parallel evolutionary test case generation for Web applications. The experimental results show that (1) compared with the global parallelization model, the evolutionary algorithm based on the parallel island model has a greater improvement in test case generation performance. In more detail, when generating test cases with the same server‐side sensitive paths coverage, the number of iterations required is reduced by 49.6%, and the time cost is reduced by 58.7%; (2) for the test case generation based on the parallel island model, if the migration scale is large, appropriately increasing the migration frequency can reduce its time cost; (3) if the number of subpopulations is fixed, appropriately increasing the number of browser processes can reduce the time cost of Web application test case evolution, but the number of browser processes should not be too large; otherwise, it may increase the time cost.