Empirical analysis of change metrics for software fault prediction
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Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
Pergamon-elsevier Science Ltd
Abstract
A quality assurance activity, known as software fault prediction, can reduce development costs arid improve software quality. The objective of this study is to investigate change metrics in conjunction with code metrics to improve the performance of fault prediction models. Experimental studies are performed on different versions of Eclipse projects and change metrics are extracted from the GIT repositories. In addition to the existing change metrics, several new change metrics are defined and collected from the Eclipse project repository. Machine learning algorithms are applied in conjunction with the change and source code metrics to build fault prediction models. The classification model with new change metrics performs better than the models using existing change metrics. In this work, the experimental results demonstrate that change metrics have a positive impact on the performance of fault prediction models, and high-performance models can be built with several change metrics. (C) 2018 Elsevier Ltd. All rights reserved.
Description
Mishra, Alok/0000-0003-1275-2050; Kumar, Sandeep/0000-0002-7008-4735; Catal, Cagatay/0000-0003-0959-2930; Kumar, Sandeep/0000-0002-3250-4866; Kumar, Dr Sandeep/0000-0003-0747-6776; Kumar, Kuldeep/0000-0003-1160-9092; Kumar, Sandeep/0000-0001-9633-407X
Keywords
Software fault prediction, Eclipse, Change log, Metrics, Software quality, Defect prediction
Turkish CoHE Thesis Center URL
Citation
41
WoS Q
Q2
Scopus Q
Source
Volume
67
Issue
Start Page
15
End Page
24