Zero‐inflated prediction model in software‐fault data. Issue 1 (1st February 2016)

• Record Type: Journal Article
• Authors: Fagundes, Roberta A.A.; Souza, Renata M.C.R.; Cysneiros, Francisco J.A.
• Is Part Of: IET software. Volume 10:Issue 1(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-02-01
• Subjects: software fault tolerance -- learning (artificial intelligence) -- statistical analysis; zero-inflated prediction model -- software-fault data -- software estimation area -- two-component prediction approach -- flexible structure -- NASA repository -- machine learning -- statistical literature; Computer software -- Periodicals; Software engineering -- Periodicals; 005.1
• DOI: 10.1049/iet-sen.2014.0067
• Languages: English
• ISSNs: 1751-8806

Abstract

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.