文章基本信息

标题：Benchmarking Machine Learning Techniques for Software Defect Detection
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作者：Saiqa Aleem ; Luiz Fernando Capretz ; Faheem Ahmed 等
期刊名称：International Journal of Software Engineering & Applications (IJSEA)
印刷版ISSN：0976-2221
电子版ISSN：0975-9018
出版年度：2015
卷号：6
期号：3
页码：11
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Machine Learning approaches are good in solving problems that have less information. In most cases, thesoftware domain problems characterize as a process of learning that depend on the various circumstancesand changes accordingly. A predictive model is constructed by using machine learning approaches andclassified them into defective and non-defective modules. Machine learning techniques help developers toretrieve useful information after the classification and enable them to analyse data from differentperspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. Thisstudy used public available data sets of software modules and provides comparative performance analysisof different machine learning techniques for software bug prediction. Results showed most of the machinelearning methods performed well on software bug datasets.
关键词：Machine Learning Methods; Software Bug Detection; Software Analytics; Predictive Analytics