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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2019

14.07.2018 | Regular Paper

Performance and cost-effectiveness of change burst metrics in predicting software faults

verfasst von: Malanga Kennedy Ndenga, Ivaylo Ganchev, Jean Mehat, Franklin Wabwoba, Herman Akdag

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2019

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Abstract

The purpose of this study is to determine a type of software metric at file level exhibiting the best prediction performance. Studies have shown that software process metrics are better predictors of software faults than software product metrics. However, there is need for a specific software process metric which can guarantee the best fault prediction performances consistently across different experimental contexts. We collected software metrics data from Open Source Software projects. We used logistic regression and linear regression algorithms to predict bug status and number of bugs corresponding to a file, respectively. The prediction performance of these models was evaluated against numerical and graphical prediction model performance measures. We found that change burst metrics exhibit the best numerical performance measures and have the highest fault detection probability and least cost of misclassification of software components.
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Fußnoten
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Metadaten
Titel
Performance and cost-effectiveness of change burst metrics in predicting software faults
verfasst von
Malanga Kennedy Ndenga
Ivaylo Ganchev
Jean Mehat
Franklin Wabwoba
Herman Akdag
Publikationsdatum
14.07.2018
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1241-7

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