Fault Prediction in Object-Oriented Software Using Neural Network Techniques

To remain competitive in the dynamic world of software development, organizations must optimize the usage of their limited resources to deliver quality products on time and within budget. This requires prevention of fault introduction and quick discovery and repair of residual faults. In this paper a new approach for predicting and classification of faults in object-oriented software systems is introduced. In particular, faults due to the use of inheritance and polymorphism are considered as they account for significant portion of faults in object-oriented systems. The proposed fault prediction model is based on supervised learning using Multilayer Perceptron Neural Network. The results of fault prediction are analyzed in terms of classification correctness and some other standard criteria. Based on the results of classification, faulty classes are further analyzed and classified according to the particular type of fault. The classification model is based on clustering using Radial-Basis Function Neural Network. It is concluded, that the proposed model provides high accuracy in discrimination between faulty and fault-free classes.