dc.contributor.author | Jorayeva, Manzura | |
dc.contributor.author | Akbulut, Akhan | |
dc.contributor.author | Catal, Cagatay | |
dc.contributor.author | Mishra, Alok | |
dc.date.accessioned | 2024-04-24T07:19:11Z | |
dc.date.available | 2024-04-24T07:19:11Z | |
dc.date.created | 2022-06-20T14:34:39Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Sensors. 2022, 22 (13), 4734. | en_US |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | https://hdl.handle.net/11250/3127846 | |
dc.description.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. Keywords: software defect prediction; software fault prediction; mobile application; Android applications; deep learning; machine learning. | en_US |
dc.language.iso | eng | en_US |
dc.relation.uri | https://doi.org/10.3390/s22134734 | |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Deep learning-based defect prediction for mobile applications | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.volume | 22 | en_US |
dc.source.journal | Sensors | en_US |
dc.source.issue | 13 | en_US |
dc.identifier.doi | 10.3390/s22134734 | |
dc.identifier.cristin | 2033513 | |
dc.source.articlenumber | 4734 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |