Feature extraction is an important step in software defect prediction technology research. However, the existing feature extraction cannot accurately obtain the nonlinear dependence relations among features, thus these methods are unable to improve the accuracy of software defect prediction model. In this paper, to solve this question we propose a software defect prediction model (Denoising Autoencoder Support Vector Machine, DA-SVM) which is based on denoising autoencoder and Support Vector Machine. Firstly, the model extracts features by using denoising autoencoder, secondly uses these features as input of support vector machine, lastly, uses this model to predict bugs. Experimental results show that DA-SVM not only improves the accuracy of software defect prediction model, but also reduces the noise of history data and enhances the robustness of the software defect prediction model.
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