SFIDMT-ART: A metamorphic group generation method based on Adaptive Rando

The performance of metamorphic testing relates strongly to the quality of test cases. However, most related research has only focused on source test cases, ignoring follow-up test cases to some extent. In this paper, we identify a potential problem that may be encountered with existing metamorphic group generation algorithms. We then propose a possible solution to address this problem. Based on this solution, we design a new algorithm for generating effective source and follow-up test cases. To improve the performance (test effectiveness and efficiency) of metamorphic testing. We introduce the concept of the input-domain difference problem, which is likely to affect the performance of metamorphic group generation algorithms. We propose a new test-case distribution criterion for metamorphic testing to address this problem. Based on our proposed criterion, we further present a new metamorphic group generation algorithm, from a black-box perspective, with new distance metrics to facilitate this algorithm. Our algorithm performs significantly better than existing algorithms, in terms of test effectiveness, efficiency and test-case diversity. Through experiments, we find that the input-domain difference problem is likely to affect the performance of metamorphic group generation algorithms. The experimental results demonstrate that our algorithm can achieve good test efficiency, effectiveness, and test-case diversity.