An Enhanced Adaptive Random Testing by Dividing Dimensions Independently

Abstract

Random testing (RT) is widely applied in the area of software testing due to its advantages such as simplicity, unbiasedness, and easy implementation. Adaptive random testing (ART) enhances RT. It improves the effectiveness of RT by distributing test cases as evenly as possible. Fixed Size Candidate Set (FSCS) is one of the most well-known ART algorithms. Its high failure-detection effectiveness only shows at low failure rates in low-dimensional spaces. In order to solve this problem, the boundary effect of the test case distribution is analyzed, and the FSCS algorithm of a limited candidate set (LCS-FSCS) is proposed. By utilizing the information gathered from success test cases (no failure-causing test inputs), a tabu generation domain of candidate test case is produced. This tabu generation domain is eliminated from the current candidate test case generation domain. Finally, the number of test cases at the boundary is reduced by constraining the candidate test case generation domain. The boundary effect is effectively relieved, and the distribution of test cases is more even. The results of the simulation experiment show that the failure-detection effectiveness of LCS-FSCS is significantly improved in high-dimensional spaces. Meanwhile, the failure-detection effectiveness is also improved for high failure rates and the gap of failure-detection effectiveness between different failure rates is narrowed. The results of an experiment conducted on some real-life programs show that LCS-FSCS is less effective than FSCS only when the failure distribution is concentrated on the boundary. In general, the effectiveness of LCS-FSCS is higher than that of FSCS.