Optimal machine learning model for software defect prediction
Автор: Tripti Lamba, Kavita, A.K.Mishra
Статья в выпуске: 2 vol.11, 2019 года.
Machine Learning is a division of Artificial Intelligence which builds a system that learns from the data. Machine learning has the capability of taking the raw data from the repository which can do the computation and can predict the software bug. It is always desirable to detect the software bug at the earliest so that time and cost can be reduced. Feature selection technique wrapper and filter method is used to find the most optimal software metrics. The main aim of the paper is to find the best model for the software bug prediction. In this paper machine learning techniques linear Regression, Random Forest, Neural Network, Support Vector Machine, Decision Tree, Decision Stump are used and comparative analysis has been done using performance parameters such as correlation, R-squared, mean square error, accuracy for software modules named as ant, ivy, tomcat, berek, camel, lucene, poi, synapse and velocity. Support vector machine outperform as compare to other machine learning model.
Linear Regression, Random Forest, Neural Network, Support Vector Machine, Decision Tree, Decision Stump
Короткий адрес: https://readera.ru/15016571
IDR: 15016571 | DOI: 10.5815/ijisa.2019.02.05
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