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

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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.

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Authors and Affiliations

  1. University School of Information and Communication Technology (U.S.I.C.T), Guru Gobind Singh Indraprastha University (G.G.S.I.P.U.), New Delhi, India

    Arvinder Kaur, Kamaldeep Kaur & Deepti Chopra

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  1. Arvinder Kaur
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  2. Kamaldeep Kaur
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  3. Deepti Chopra
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Correspondence to Deepti Chopra.

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Kaur, A., Kaur, K. & Chopra, D. An empirical study of software entropy based bug prediction using machine learning. Int J Syst Assur Eng Manag 8 (Suppl 2), 599–616 (2017). https://doi.org/10.1007/s13198-016-0479-2

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  • DOI: https://doi.org/10.1007/s13198-016-0479-2

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