Code Coverage Similarity Measurement Using Machine Learning for Test Cases Minimization

Abstract

Machine learning approach for minimizing the number of test cases on the test suite is an interesting research area on software testing. The research tries to minimize the number of test cases on the test suite by minimizing redundant test cases based on similarity classification. The Support Vector Machine, K-Nearest Neighbour, and Decision tree classify similar test cases by comparing the lines executed by test cases. The result has shown that the support vector machine is the highest score on accuracy and the lowest score on error rate comparing with K-Nearest Neighbour, and Decision tree. Minimize the redundant test cases increase the quality of the test cases, and reducing time on the testing process.