Prediction of Software Reliability Using Connectionist Models
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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.