Open AccessZero‐inflated prediction model in software‐fault data
Author(s) -
Fagundes Roberta A.A.,
Souza Renata M.C.R.,
Cysneiros Francisco J.A.
Publication year - 2016
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2014.0067
Subject(s) - computer science , zero (linguistics) , software , fault (geology) , software quality , reliability engineering , software engineering , programming language , software development , engineering , geology , seismology , philosophy , linguistics
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.