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Please use this identifier to cite or link to this item: http://dspace.univ-ouargla.dz/jspui/handle/123456789/20983

Titre: On The Use Of KStar Algorithm For Predicting Object-Oriented Software Maintainability
Auteur(s): Zighed, Narimane
Bounour, Nora
Mots-clés: Machine Learning Techniques
Software Maintainability
Object-Oriented Metrics
Issue Date: 5-Mar-2019
Editeur: Université Kasdi Merbah Ouargla
Collection/Numéro: 2019;
Résumé: This paper presents an ongoing work on using KStar algorithm to predict Object-Oriented software maintainability. The maintainability is measured as the number of changes made to code throughout the maintenance period by using Object-Oriented software metrics. We build a prediction model based on data collected from two different Object-Oriented systems. However, to figure out the advantages of KStar algorithm we made five experiments with the Weka machine learning workbench and to compare our proposed model with the other algorithms which are (linear Regression, Neural Network, Decision Tree, SVM), the prediction accuracy of all models is evaluated and compared using cross-validation and different types of accuracy measures. As a result, KStar yields better results and it demonstrated to be the best of them to predict more accurately than the other typical techniques.
Description: Le 2eme Conference Internationale sur intelligence Artificielle et les Technologies Information ICAIIT 2019
URI: http://dspace.univ-ouargla.dz/jspui/handle/123456789/20983
Appears in Collections:2. Faculté des nouvelles technologies de l’information et de la communication

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