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A Survey on Software Defect Prediction Using Deep Learning

Author

Listed:
  • Elena N. Akimova

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
    Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia)

  • Alexander Yu. Bersenev

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
    Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia)

  • Artem A. Deikov

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
    Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia)

  • Konstantin S. Kobylkin

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
    Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia)

  • Anton V. Konygin

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia)

  • Ilya P. Mezentsev

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
    Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia)

  • Vladimir E. Misilov

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
    Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia)

Abstract

Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field.

Suggested Citation

  • Elena N. Akimova & Alexander Yu. Bersenev & Artem A. Deikov & Konstantin S. Kobylkin & Anton V. Konygin & Ilya P. Mezentsev & Vladimir E. Misilov, 2021. "A Survey on Software Defect Prediction Using Deep Learning," Mathematics, MDPI, vol. 9(11), pages 1-14, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1180-:d:560717
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    Citations

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    Cited by:

    1. Aimen Khalid & Gran Badshah & Nasir Ayub & Muhammad Shiraz & Mohamed Ghouse, 2023. "Software Defect Prediction Analysis Using Machine Learning Techniques," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    2. Ahmed Abdu & Zhengjun Zhai & Redhwan Algabri & Hakim A. Abdo & Kotiba Hamad & Mugahed A. Al-antari, 2022. "Deep Learning-Based Software Defect Prediction via Semantic Key Features of Source Code—Systematic Survey," Mathematics, MDPI, vol. 10(17), pages 1-26, August.

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