A Survey on Software Defect Prediction Using Deep Learning

Elena N. Akimova, Alexander Yu. Bersenev, Artem A. Deikov, Konstantin S. Kobylkin, Anton V. Konygin, Ilya P. Mezentsev and Vladimir E. Misilov
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Elena N. Akimova: Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
Alexander Yu. Bersenev: Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
Artem A. Deikov: Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
Konstantin S. Kobylkin: Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 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
Vladimir E. Misilov: Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia

Mathematics, 2021, vol. 9, issue 11, 1-14

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.

Keywords: defect prediction; anomaly detection; program analysis; code understanding; neural networks; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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