Cost-benefit evaluation model for automated testing based on test case prioritization

Abstract

Cost-benefit evaluation is an important factor to determine whether a project is suitable for automated testing. This study begins with a discussion on benefits and limitations of automated testing and issues that should be considered before implementation. Based on major influencing factors, this study proposes a novel cost-benefit model for automated testing. In this model, K-means algorithm is proposed to determine the prioritization of automated test cases and the principle of production possibilities frontier is introduced to select the proportion of automated test cases. Experiment results show that this model achieves a more accurate evaluation and the classification and selection of test cases improve the accuracy of this model.