Deep Learning-Based Defect Prediction for Mobile Applications

Deep Learning-Based Defect Prediction for Mobile Applications

Blog Article

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 Pentair EasyTouch Parts applications.We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and Rice Cooker 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.