Zero‐inflated prediction model in software‐fault data

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.

Insights on Research Techniques towards Cost Estimation in Software Design

“…The advantage is to identify the possible faults even before it could possibly inflict some serious problems in the software design. Study addressing such problem can be found in the work carried out by Fagundes et al [50] where a predictive principle is introduced by the author. The technique jointly uses both parametric as well as non-parametric frameworks for enhancing the accuracy of fault prediction.…”