DEFECT PREDICTION BA SED ON QUANTIT ATIVE AND QUALITATIVE FACTORS USING PSO OPTIMIZED NEURAL NETWORK

An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way a biological nervous system in human brain works. Large number of neurons present in the human brain forms the key element of the neural network paradigm and act as elementary processing elements. Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO focuses on the issues like stability, convergence, and explosion of the swarm. In this paper, we have done the training of the neural network with the help of PSO technique for Defect Prediction Based on Quantitative and Qualitative Factors. Data is taken from NASA named PC1 fault dataset. Norman Fenton, et al in developed a causal model that includes such process factors, both quantitative and qualitative factors [8]. A measurable and precise definition of what faults are makes it possible to accurately identify and count them, which in turn allows the formulation of models relating fault counts and types to other measurable attributes of a software system. The incomplete and ambiguous nature of current fault definitions adds a noise component to the inputs used in modeling fault content, If this noise component is sufficiently large, any attempt to develop a fault model will produce invalid results. With modern configuration management tools, the identification and counting of software faults can be automated [11].