What is it about?
Subsequent releases of a system have common development environments and characteristics. However, prediction models based on within-project data potentially suffer from being based on fault data reported within relatively short maintenance time intervals, which potentially decreases their prediction abilities. In this paper, we propose an approach that improves the classification performance of models based on within-project data that are applied to predict the fault-proneness of the classes in a software post-release .
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Why is it important?
The classification performance of the categorization-based fault-proneness prediction models is considerably better than those constructed using existing approaches.
Perspectives
The results demonstrate that the classification performance of the categorization-based models is much better than those that are based on the other approaches. These results encourage software engineers to consider the proposed approach when predicting the fault-proneness of classes in a post-release of a system that has several pre-releases with available fault data.
Jehad Aldallal
Kuwait University
Read the Original
This page is a summary of: A Categorization-Based Approach for Predicting the Fault-Proneness of Object-Oriented Classes in Software Post-Releases, IET Software, May 2020, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-sen.2019.0326.
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