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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library,  Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2014 Vol.2(2): 176-180 ISSN: 2301-3559
DOI: 10.7763/LNSE.2014.V2.118

Empirical Evaluation of Machine Learning Algorithms for Fault Prediction

Arvinder Kaur and Inderpreet Kaur
Abstract—Producing quality software is a very challenging task looking at the size and complexity of software developed these days. Predicting software quality early helps in using testing resources optimally. So, many statistical and machine learning techniques are used to predict quality classes in software. In this work, six machine learning classifiers have been used to estimate the fault proneness of 5885 classes used in five open source software on the basis of object-oriented metrics calculated on these classes. Bagging and J48 classifiers turn out to be the best one amongst the classifiers used.

Index Terms—Classifiers, fault proneness, object-oreiented software metrics, quality metrics.

Arvinder Kaur is with University School of Information and Communication Technology , Department of Information and Technology, Guru Gobind Singh Indraprastha University, Delhi, India (e-mail: arvinder70@gmail.com ).
Inderpreet Kaur is with Department of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India (e-mail: inderpreet_2772@yahoo.co.in).


Cite: Arvinder Kaur and Inderpreet Kaur, "Empirical Evaluation of Machine Learning Algorithms for Fault Prediction," Lecture Notes on Software Engineering vol. 2, no. 2, pp. 176-180, 2014.

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