Fault-Prone Module Prediction of a Web Application using Artificial Neural Networks

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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