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AuthorJorayeva, Manzura
AuthorAkbulut, Akhan
AuthorCatal, Cagatay
AuthorMishra, Alok
Available date2022-11-30T11:23:19Z
Publication Date2022
Publication NameSensors
ResourceScopus
Resource2-s2.0-85132376127
URIhttp://dx.doi.org/10.3390/s22134734
URIhttp://hdl.handle.net/10576/36787
AbstractSmartphones 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. 2022 by the authors. Licensee MDPI, Basel, Switzerland.
SponsorFunding: This research was funded by Molde University College-Specialized Univ. in Logistics, Norway for the support of Open Access fund.
Languageen
PublisherMDPI
SubjectAndroid applications; deep learning; machine learning; mobile application; software defect prediction; software fault prediction
TitleDeep Learning-Based Defect Prediction for Mobile Applications
TypeArticle
Issue Number13
Volume Number22
dc.accessType Open Access


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