A Test Case Recommendation Method Based on Morphological Analysis, Clustering and the Mahalanobis-Taguchi Method
Hirohisa Aman, Takashi Nakano, Hideto Ogasawara, Minoru Kawahara · 2017
This paper focuses on the content of test cases, and categorizes test cases into clusters using the similarity between test cases, their degree of similarity is obtained through a morphological analysis. If there are two similar test cases, they would test the same or similar functionalities in similar but different conditions. Thus, when one of them is run for a regression testing, the remaining one should be run as well, in order to reduce a risk of overlooking regressions. Once a test engineer decides to run a set of test cases, the method proposed in this paper can recommend adding similar test cases to their candidate set. The proposed method also considers the priorities of recommended test cases by using the Mahalanobis-Taguchi method. This paper reports on an empirical study with an industrial software product. The results show that the proposed method is useful to prevent overlooking regressions.