C.S. Reddy, K.V.S.V.N. Raju, V.V. Kumari, and G.L. Devi (India)
Artificial neural network, Error back propagation algorithm, Multilayer perceptron, Principal component analysis, Supervised learning.
The problem addressed in this research is the prediction of fault-prone modules in a web application using Artificial Neural Networks. Past research in this area focused on applications related to procedural paradigm and object-oriented paradigm. In this paper, we turned our attention to applying Artificial Neural Networks to fault module prediction of a web application. In our research, we implemented Principal Component Analysis technique and Error Back propagation training algorithm. The modules are classified into two classes- fault-prone and not fault-prone using web application quality metric data. The proposed model is based on supervised learning using Multilayer Perceptron Neural Network.
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