Deep Learning-Based Defect Prediction for Mobile Applications.

Journal: Sensors (Basel, Switzerland)
Published Date: Jun 23, 2022

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.

Authors

  • Manzura Jorayeva
    Department of Computer Engineering, Istanbul Kültür University, Istanbul 34158, Turkey.
  • Akhan Akbulut
    Department of Computer Science, North Carolina State University, Raleigh, NC 27606, USA; Department of Computer Engineering, Istanbul Kultur University, Atakoy Campus Bakirkoy, Istanbul 34156, Turkey. Electronic address: aakbulu@ncsu.edu.
  • Cagatay Catal
    Department of Computer Engineering, Bahcesehir University, Istanbul 34353, Turkey.
  • Alok Mishra
    Faculty of Logistics, Molde University College-Specialized University in Logistics, Molde 6402, Norway.

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