Empirical study of Software Quality estimation

Authors:
Inderpreet Kaur;Arvinder Kaur
Affiliations:
Guru Gobind Singh Indraprastha University, Delhi;Guru Gobind Singh Indraprashta University, Dwarka, Delhi
Venue:
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Year:
2012

Citing 27
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Abstract

Software Quality is an important nonfunctional requirement which is not satisfied by many software products. Prediction models using object oriented metrics can be used to identify the faulty classes. In this paper, we will empirically study the relationship between object oriented metrics and fault proneness of an open source project Emma. Twelve machine Learning classifiers have been used. Univariate and Multivariate analysis of Emma shows that Random Forest provides optimum values for accuracy, precision, sensitivity and specificity.