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An empirical study of software entropy based bug prediction using machine learning

Author

Listed:
  • Arvinder Kaur

    (Guru Gobind Singh Indraprastha University (G.G.S.I.P.U.))

  • Kamaldeep Kaur

    (Guru Gobind Singh Indraprastha University (G.G.S.I.P.U.))

  • Deepti Chopra

    (Guru Gobind Singh Indraprastha University (G.G.S.I.P.U.))

Abstract

There are many approaches for predicting bugs in software systems. A popular approach for bug prediction is using entropy of changes as proposed by Hassan (2009). This paper uses the metrics derived using entropy of changes to compare five machine learning techniques, namely Gene Expression Programming (GEP), General Regression Neural Network, Locally Weighted Regression, Support Vector Regression (SVR) and Least Median Square Regression for predicting bugs. Four software subsystems: mozilla/layout/generic, mozilla/layout/forms, apache/httpd/modules/ssl and apache/httpd/modules/mappers are used for the validation purpose. The data extraction for the validation purpose is automated by developing an algorithm that employs web scraping and regular expressions. The study suggests GEP and SVR as stable regression techniques for bug prediction using entropy of changes.

Suggested Citation

  • Arvinder Kaur & Kamaldeep Kaur & Deepti Chopra, 2017. "An empirical study of software entropy based bug prediction using machine learning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 599-616, November.
    • Handle: RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0479-2
    DOI: 10.1007/s13198-016-0479-2
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