A quality assurance activity, known as software fault prediction, can reduce development costs and improve software quality. The objective of this study is to investigate change metrics in conjunction with code metrics to improve the performance of fault prediction models. Experimental studies are performed on different versions of Eclipse projects and change metrics are extracted from the GIT repositories. In addition to the existing change metrics, several new change metrics are defined and collected from the Eclipse project repository. Machine learning algorithms are applied in conjunction with the change and source code metrics to build fault prediction models. The classification model with new change metrics performs better than the models using existing change metrics. In this work, the experimental results demonstrate that change metrics have a positive impact on the performance of fault prediction models, and high-performance models can be built with several change metrics.
There are no comments yet. You can post the first one!
Post a comment
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.