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Deep Learning-Based Defect Prediction for Mobile Applications.

Manzura Jorayeva1,2, Akhan Akbulut1, Cagatay Catal3

  • 1Department of Computer Engineering, Istanbul Kültür University, Istanbul 34158, Turkey.

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|July 9, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a defect prediction model for mobile applications using deep learning. The convolutional neural network (CNN) model achieved high accuracy within projects, improving software quality and user experience.

Keywords:
Android applicationsdeep learningmachine learningmobile applicationsoftware defect predictionsoftware fault prediction

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Mobile Computing

Background:

  • Mobile applications are prevalent, but defects negatively impact user experience and businesses.
  • Early detection and removal of mobile application defects are crucial before release.

Purpose of the Study:

  • To develop a robust defect prediction model for mobile applications.
  • To evaluate the effectiveness of deep learning algorithms for defect prediction in Android applications.

Main Methods:

  • Utilized deep learning algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM).
  • Conducted within-project and cross-project experiments for model validation.
  • Focused on Android-based mobile applications.

Main Results:

  • The CNN-based model demonstrated superior performance in within-project defect prediction, achieving an average Area Under the ROC Curve (AUC) of 0.933.
  • Cross-project defect prediction using deep learning shows potential but requires further enhancement.

Conclusions:

  • Deep learning, particularly CNNs, is effective for within-project mobile application defect prediction.
  • Further research is needed to improve the accuracy of cross-project defect prediction models for mobile applications.