Abstract. With the process of software development and application basing on network and cloud, the change of software development requires new software defect prediction method for these kinds of software development, which can solve the problems of the traditional software defect prediction method based on target project, such as the same predict background and higher cost of defect tagging. A new software defect prediction method based on multi source data oriented network and cloud development process is proposed. This method selects the predictive candidates from multisource projects which have similar characteristics as objective projects, and then guides the training data selection by the software modules, finishes the prediction based on Naive Bayesian algorithm. Finally through algorithm experiment this method is proved superior to the traditional WP prediction model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.