The quality evaluation of open source software (OSS) products, e.g., defect estimation and prediction approaches of individual releases, gains importance with increasing OSS adoption in industry applications. Most empirical studies on the accuracy of defect prediction and software maintenance focus on product metrics as predictors that are available only when the product is finished. Only few prediction models consider information on the development process (project metrics) that seems relevant to quality improvement of the software product. In this paper, we investigate defect prediction with data from a family of widely used OSS projects based both on product and project metrics as well as on combinations of these metrics. Main results of data analysis are (a) a set of project metrics prior to product release that had strong correlation to potential defect growth between releases and (b) a combination of product and project metrics enables a more accurate defect prediction than the application of one single type of measurement. Thus, the combined application of project and product metrics can (a) improve the accuracy of defect prediction, (b) enable a better guidance of the release process from project management point of view, and (c) help identifying areas for product and process improvement.
REF constructed the model to predict defects using product and project metrics collected from design review, code testing, code peer review as well as product release usage and defect validation.
Defect Prediction using Combined Product and Project Metrics - A Case Study from the Open Source "Apache" MyFaces Project Family
2008 34th Euromicro Conference Software Engineering and Advanced Applications
2008 34th Euromicro Conference Software Engineering and Advanced Applications
Computer Science