MANJULA C, Lilly Florence


Abstract: Software defect prediction is considered as most interesting area for researchers in the field of software engineering. This process of defect prediction, identifies the bug during automated testing process which prevents the development of faulty software module. According to this process, previous archives of software modules are considered for analyzing the quality based on data mining and machine leaning concepts, which identifies the faults in software modules. Several techniques have been presented for software defect prediction using data mining techniques and machine learning techniques but achieving desired accuracy in performance is a challenging task for researchers. To address this issue, in this work we have presented a new approach for software defect prediction by combining genetic algorithm optimization process for feature subspace reduction and deep belief network for pattern learning. Deep belief networks are further improved by applying L1-regularization scheme resulting in better learning process by reducing the overfitting error. This combined model is implemented on SPIE lab software defect database. An extensive experimental study is carried out which shows that proposed approach achieves higher accuracy when compared with state-of-the-art software defect prediction techniques.


Keywords: Software Defect Prediction, Machine Learning, Deep Belief Network, Genetic Algorithm, L1-regularization

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