JORAYEVA, MANZURAAKBULUT, AKHANÇatal, ÇağatayMishra, Alok2023-03-222023-03-222022Jorayeva M, Akbulut A, Catal C, Mishra A. Deep Learning-Based Defect Prediction for Mobile Applications. Sensors. 2022; 22(13):4734. https://doi.org/10.3390/s22134734https://doi.org/10.3390/s22134734https://hdl.handle.net/11413/8395Smartphones 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.eninfo:eu-repo/semantics/openAccessAttribution-NoDerivs 3.0 United Stateshttp://creativecommons.org/licenses/by-nd/3.0/us/Software Defect PredictionSoftware Fault PredictionMobile ApplicationAndroid ApplicationsDeep LearningMachine LearningArticle0008234716000012-s2.0-85132376127358082302319-6440