Testing a group of software artifacts that implement the same specification is time consuming, especially when the test case repository is large. In the meantime, some of test cases may cover the same aspects in the software under test, and thus it is not necessary to apply all the test cases. This paper proposes a Model-based Adaptive Test (MAT) case selection and ranking technique to eliminate redundant test cases, and rank the test cases according to their potency and coverage. This technique can be applied in various domains where multiple versions of an application are available for testing, such as web service group testing, n-version applications, regression testing, and specification-based application testing. MAT is a statistical model based on earlier testing results, and the model can accurately determine the next sets of test cases to minimize the testing effort. It can be applied to testing of multi-versioned web services, and the results shows that MAT can reduce testing effort while still maintain the effectiveness of testing.
ASJC Scopus subject areas
- Computer Science(all)