New Similarity Function for Test Case Prioritization in Model-Based Context

Abstract

Test case prioritization (TCP) refers to the process, which evaluates test cases ranking within a test suite. Currently, the focus of TCP research lay mainly in code-based testing. However, the Model-Based Testing (MBT) context still need more investigation. This paper presents a new distance function for defining the degree of similarity between MBT generated test cases. These test cases are represented by labeled graphs, and their similarity is measured. In the paper, the function is applied in Hint-Based Adaptive Random Prioritization method, and improved fault-detection performance is presented.