Comparative Performance Analysis of Machine Learning Techniques for Software Bug Detection

نویسندگان

  • David C. Wyld
  • Saiqa Aleem
  • Luiz Fernando Capretz
  • Faheem Ahmed
چکیده

Machine learning techniques can be used to analyse data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. In this paper, a comparative performance analysis of different machine learning techniques is explored for software bug prediction on public available data sets. Results showed most of the machine learning methods performed well on software bug datasets.

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تاریخ انتشار 2014