Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/102859
Title: A survey on software defect prediction using deep learning
Authors: Akimova, E. N.
Bersenev, A. Yu.
Deikov, A. A.
Kobylkin, K. S.
Konygin, A. V.
Mezentsev, I. P.
Misilov, V. E.
Issue Date: 2021
Publisher: MDPI AG
Citation: A survey on software defect prediction using deep learning / E. N. Akimova, A. Yu. Bersenev, A. A. Deikov, et al. — DOI 10.3390/math9111180 // Mathematics. — 2021. — Vol. 9. — Iss. 11. — 1180.
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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords: ANOMALY DETECTION
CODE UNDERSTANDING
DEEP LEARNING
DEFECT PREDICTION
NEURAL NETWORKS
PROGRAM ANALYSIS
URI: http://hdl.handle.net/10995/102859
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85107438889
PURE ID: 22106357
7858c6fd-60db-4336-9442-b86674a3a3ae
ISSN: 22277390
DOI: 10.3390/math9111180
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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