Comparative analysis of software reliability predictions using statistical and machine learning methods
by Pradeep Kumar; Yogesh Singh
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 12, No. 3/4, 2013

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.

Online publication date: Wed, 25-Sep-2013

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com