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Predicting Stability of Open-Source Software Systems Using Combination of Bayesian Classifiers

Predicting Stability of Open-Source Software Systems Using Combination of Bayesian Classifiers Predicting Stability of Open-Source Software Systems Using Combination of Bayesian Classifiers SALAH BOUKTIF, UAE University, UAE HOUARI SAHRAOUI, University of Montreal, Canada FAHEEM AHMED, Thompson Rivers University, Canada The use of free and Open-Source Software (OSS) systems is gaining momentum. Organizations are also now adopting OSS, despite some reservations, particularly about the quality issues. Stability of software is one of the main features in software quality management that needs to be understood and accurately predicted. It deals with the impact resulting from software changes and argues that stable components lead to a cost-effective software evolution. Changes are most common phenomena present in OSS in comparison to proprietary software. This makes OSS system evolution a rich context to study and predict stability. Our objective in this work is to build stability prediction models that are not only accurate but also interpretable, that is, able to explain the link between the architectural aspects of a software component and its stability behavior in the context of OSS. Therefore, we propose a new approach based on classifiers combination capable of preserving prediction interpretability. Our approach is classifier-structure dependent. Therefore, we propose a particular solution for combining Bayesian classifiers in order to derive http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

Predicting Stability of Open-Source Software Systems Using Combination of Bayesian Classifiers

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
2158-656X
DOI
10.1145/2555596
Publisher site
See Article on Publisher Site

Abstract

Predicting Stability of Open-Source Software Systems Using Combination of Bayesian Classifiers SALAH BOUKTIF, UAE University, UAE HOUARI SAHRAOUI, University of Montreal, Canada FAHEEM AHMED, Thompson Rivers University, Canada The use of free and Open-Source Software (OSS) systems is gaining momentum. Organizations are also now adopting OSS, despite some reservations, particularly about the quality issues. Stability of software is one of the main features in software quality management that needs to be understood and accurately predicted. It deals with the impact resulting from software changes and argues that stable components lead to a cost-effective software evolution. Changes are most common phenomena present in OSS in comparison to proprietary software. This makes OSS system evolution a rich context to study and predict stability. Our objective in this work is to build stability prediction models that are not only accurate but also interpretable, that is, able to explain the link between the architectural aspects of a software component and its stability behavior in the context of OSS. Therefore, we propose a new approach based on classifiers combination capable of preserving prediction interpretability. Our approach is classifier-structure dependent. Therefore, we propose a particular solution for combining Bayesian classifiers in order to derive

Journal

ACM Transactions on Management Information Systems (TMIS)Association for Computing Machinery

Published: Apr 1, 2014

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