Abstract
Software fault data with many zeroes in addition to large non-zero values are common in the software estimation area. A two-component prediction approach that provides a robust way to predict this type of data is introduced in this study. This approach allows to combine parametric and non-parametric models to improve the prediction accuracy. This way provides a more flexible structure to understand data. To show the usefulness of the proposed approach, experiments using eight projects from the NASA repository are considered. In addition, this method is compared with methods from the machine learning and statistical literature. The performance of the methods is measured by the prediction accuracy that is assessed based on the mean magnitude of relative errors.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
