Comparative analysis of software reliability predictions using statistical and machine learning methods. (26th September 2013)

• Record Type: Journal Article
• Authors: Kumar, Pradeep; Singh, Yogesh
• Is Part Of: International journal of intelligent systems technologies and applications. Volume 12:Number 3/4(2013)
• Page Start: 230
• Page End: 253
• Publication Date: 2013-09-26
• Subjects: 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; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 006.3
• Languages: English
• ISSNs: 1740-8865

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.