Comparative analysis of genetic algorithms for automated test case generation to support software quality

Abstract

Software testing is crucial for enhancing software quality, but designing test cases is a labor-intensive, resource-intensive, and time-consuming process. Additionally, test case designers often introduce subjectivity when creating test cases manually. To address these challenges, this paper compares three different approaches for automatically generating program branch coverage test cases: the parallel data generation algorithm (PDGA), a standard genetic algorithm (SGA), and a random test generation method. By leveraging genetic algorithms and parallel data generation techniques, these automated approaches aim to reduce the manual effort, resources, and potential biases involved in test case design, while improving the efficiency and effectiveness of achieving comprehensive branch coverage during software testing. The experimental results, conducted using five datasets with programs written in PHP, demonstrate that PDGA outperforms both SGA and random methods across various tested programs, achieving higher maximum and average coverage. Specifically, PDGA achieved an average coverage of 100% in the "calculator" program, highlighting its superior stability and efficiency. While SGA also shows good performance, it is not as optimal as PDGA, and the random method shows the lowest performance among the three. These findings underscore the potential of genetic algorithms, particularly PDGA, to enhance the coverage and quality of software testing, thereby significantly improving system reliability.