Mıshra, AlokJorayeva, ManzuraAkbulut, AkhanCatal, CagatayMishra, AlokSoftware Engineering2024-07-052024-07-05202231424-822010.3390/s22134734https://doi.org/10.3390/s22134734https://hdl.handle.net/20.500.14411/1786Mishra, Alok/0000-0003-1275-2050; Catal, Cagatay/0000-0003-0959-2930Smartphones 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/openAccesssoftware defect predictionsoftware fault predictionmobile applicationAndroid applicationsdeep learningmachine learningArticleQ22213WOS:00082347160000135808230