Deep Learning-Based Defect Prediction for Mobile Applications

Abstract

Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.

Insights of effectivity analysis of learning-based approaches towards software defect prediction

“…The scheme has been implemented using multiple multi-layer perceptron (MLP) configuration over multiple standard datasets. Adoption of deep learning is also witnessed in work of Jorayeva et al [43] where LSTM is integrated with convolution neural network considering the user case of open-source Android application software defects. The analysis also has perform comparative analysis for ANN, convolution neural network (CNN), and LSTM to show that performance of CNN and LSTM is always better than ANN while CNN is slightly more better than LSTM.…”