Abstract
Software defect prediction technology plays an important role in ensuring software quality. The traditional software defect prediction model can only perform “shallow learning” and cannot perform deep mining of data features. Aiming at this problem, we use the stacked denoising auto-encoder (SDAE) to superimpose into deep neural network. First, the deep network model was built through the stacked layers of denoising auto-encoder (DAE), then the unsupervised method was used to train each layer in turn with noised input for more robust expression, characteristics were learnt supervised by back propagation (BP) neural network and the whole net was optimized by using error back propagation. Simulation experiments prove that the prediction accuracy of our SDAE model is significantly improved compared with the traditional SVM and KNN prediction model.
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