Open AccessLiterature survey of deep learning‐based vulnerability analysis on source code
Author(s) -
Semasaba Abubakar Omari Abdallah,
Zheng Wei,
Wu Xiaoxue,
Agyemang Samuel Akwasi
Publication year - 2020
Publication title -
iet software
Language(s) - English
Resource type - Journals
ISSN - 1751-8814
DOI - 10.1049/iet-sen.2020.0084
Subject(s) - computer science , vulnerability (computing) , source code , vulnerability assessment , popularity , deep learning , field (mathematics) , process (computing) , code (set theory) , focus (optics) , software , software engineering , data science , risk analysis (engineering) , artificial intelligence , computer security , programming language , set (abstract data type) , psychology , social psychology , physics , mathematics , psychological resilience , pure mathematics , optics , psychotherapist , medicine
Vulnerabilities in software source code are one of the critical issues in the realm of software code auditing. Due to their high impact, several approaches have been studied in the past few years to mitigate the damages from such vulnerabilities. Among the approaches, deep learning has gained popularity throughout the years to address such issues. In this literature survey, the authors provide an extensive review of the many works in the field software vulnerability analysis that utilise deep learning‐based techniques. The reviewed works are systemised according to their objectives (i.e. the type of vulnerability analysis aspect), the area of focus (i.e. the focus area of the analysis), what information about source code is used (i.e. the features), and what deep learning techniques they employ (i.e. what algorithm is used to process the input and produce the output). They also study the limitations of the papers and topical trends concerning vulnerability analysis.