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AuthorJorayeva, Manzura
AuthorAkbulut, Akhan
AuthorCatal, Cagatay
AuthorMishra, Alok
Available date2022-11-30T11:23:19Z
Publication Date2022
Publication NameSensors
ResourceScopus
Resource2-s2.0-85126997961
URIhttp://dx.doi.org/10.3390/s22072551
URIhttp://hdl.handle.net/10576/36788
AbstractSoftware defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naïve Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.
SponsorFunding: This research was funded by Molde University College-Specialized Univ. in Logistics, Norway for the support of Open Access fund.
Languageen
PublisherMDPI
Subjectdeep learning; machine learning; mobile application; review; software defect prediction; software fault prediction; systematic literature review
TitleMachine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review
TypeArticle Review
Issue Number7
Volume Number22
dc.accessType Open Access


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