Browsing by Author "JORAYEVA, MANZURA"

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Open Access
Deep Learning-Based Defect Prediction for Mobile Applications
(MPDI, 2022) JORAYEVA, MANZURA; AKBULUT, AKHAN; Çatal, Çağatay; Mishra, Alok
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.
Open Access
Deep Learning-Based Defect Prediction for Mobile Apps
(İstanbul Kültür Üniversitesi, 2022) JORAYEVA, MANZURA; Akhan Akbulut ; Çağatay Çatal
Mobile applications are increasing their popularity every year. However, unrecognized defects within mobile applications can affect businesses due to negative user experience. To avoid this, defects of applications should be reviewed before releases. The well-known methods for defect prevention include Review and Inspection, Walkthrough, Logging and Documentation, and Root Cause Analysis, as well as employing innovative predictive approaches using machine learning. The benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. This study aims to present a defect prediction model for mobile applications. We applied cross-project and used deep learning algorithms including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long-Short Term Memory (LSTM) to develop a defect prediction model and applied it to Android applications datasets. SMOTE Oversampling technique is used to balance datasets, accuracy metrics such as precision, recall, F1-score, ROC, and AUC to achieve performance, and model results are evaluated with tenfold cross-validation.
Open Access
Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review
(MDPI, 2022) JORAYEVA, MANZURA; AKBULUT, AKHAN; Çatal, Çağatay; Mishra, Alok
Software 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 Naive 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.