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Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review.

Manzura Jorayeva1, Akhan Akbulut1, Cagatay Catal2

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

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|April 12, 2022
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Summary
This summary is machine-generated.

This systematic literature review reveals machine learning applications in mobile software defect prediction. Supervised learning and object-oriented metrics dominate, with few studies exploring deep learning for mobile fault prediction.

Keywords:
deep learningmachine learningmobile applicationreviewsoftware defect predictionsoftware fault predictionsystematic literature review

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Mobile Computing

Background:

  • Software defect prediction aims to identify faulty components early in development.
  • Effective defect prediction optimizes resource allocation for software testing.
  • Existing research on mobile application defect prediction lacks a systematic overview.

Purpose of the Study:

  • To systematically review and evaluate the application of machine learning in predicting faults within mobile applications.
  • To identify trends, common methodologies, and prevalent algorithms in mobile software defect prediction research.

Main Methods:

  • Conducted a Systematic Literature Review (SLR).
  • Defined nine research questions to guide the review.
  • Selected and analyzed 47 relevant studies from scientific databases.

Main Results:

  • The majority of studies focused on Android applications (48%).
  • Supervised machine learning techniques were predominantly used (92%).
  • Object-Oriented metrics were the preferred choice for feature extraction, with Naïve Bayes, SVM, Logistic Regression, ANNs, and Decision Trees being the top algorithms. Deep learning methods like LSTM, DBN, and DNN were underutilized.

Conclusions:

  • This SLR is the first to systematically review mobile software defect prediction research.
  • Findings highlight the dominance of supervised learning and traditional metrics, with potential for deeper exploration of deep learning.
  • The study provides a foundation for future research and practical applications in mobile software fault prediction.