Title: Comparative analysis of software reliability predictions using statistical and machine learning methods

Authors: Pradeep Kumar; Yogesh Singh

Addresses: CS Department, IEC-CET, Greater Noida, Affiliated to UP Technical University Lucknow, India ' M.S. University of Baroda, Vadodara, Gujarat, India

Abstract: This paper examines the performance of statistical (linear regression) and machine learning methods like Radial Basis Function Network (RBFN), Generalised Regression Neural Network (GRNN), Support Vector Machine (SVM), Fuzzy Inference System (FIS), Adaptive Neuro Fuzzy Inference System (ANFIS), Gene Expression Programming (GEP), Group Method of Data Handling (GMDH) and Multivariate Adaptive Regression Splines (MARS) for predicting software reliability. The effectiveness of LR and machine learning methods are illustrated with the help of 16 failure datasets of real-life projects taken from Data and Analysis Centre for Software (DACS). Two performance measures, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), are compared quantitatively obtained from rigours experiments. We empirically demonstrate that performance of the SVM model is better than LR and other machine learning techniques in all datasets. Finally, we conclude that such methods can help in reliability prediction using real-life failure datasets.

Keywords: software reliability; machine learning; artificial neural networks; ANNs; support vector machines; SVM; fuzzy inference systems; ANFIS; group method of data handling; GMDH; fuzzy logic; gene expression programming; GEP; multivariate adaptive regression splines; reliability prediction; failure datasets.

DOI: 10.1504/IJISTA.2013.056529

International Journal of Intelligent Systems Technologies and Applications, 2013 Vol.12 No.3/4, pp.230 - 253

Received: 07 Nov 2012
Accepted: 04 Apr 2013
Published online: 25 Sep 2013 *

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