Automatic Software Testing Target Path Selection using K-means Clustering Algorithm

摘要

Path testing is an effective method of software testing. It is not realistic to achieve coverage for all paths during complex software testing. Selecting the correct paths as target paths is a key problem. A method of selecting target paths based on the K-means algorithm is presented in this study. First, we divide paths into different groups using the K-means algorithm, so that paths having high similarity are divided into the same group. Then, we choose the cluster centers as targets and ensure that the selected target paths have more considerable differentiation, which guarantees the adequacy of later testing. The experimental results demonstrate the effectiveness of the proposed method.