Learning Based Software Defect Prediction

Software Engineering is a Comprehensive domain since software system have become larger and complex than ever.Such software characteristics make it very complex to develop fault free software.Therefore, identifying defects automatically and fixing them is challenging task.Improper modelling, lack of requirement specifications, wrong coding, poor configuration management may cause defects which leads to failure of software system.Such defects must be detected and fixed at early stage of software development to minimize cost.Machine learning algorithms widely used in the software defect prediction also achieves good results in predicting software defects using deep learning techniques.In this paper, we are providing comparative study of various algorithms like convolutional neural networks, multi-layer Perceptrons in identifying defects in software.Moreover, the experiments conducted on NASA datasets.