AuthorsA. Grishina
TitleEnabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods
AfilliationSoftware Engineering
Project(s)Data-Driven Software Engineering Department
Publication TypeTechnical reports
Year of Publication2022
Pagination3 pages
Date Published02/2022
Keywordsautomatic program repair, graph-based machine learning, ml4code, natural language processing, software security, static analysis

Users around the world rely on software-intensive systems in their day-to-day activities. These systems regularly contain bugs and security vulnerabilities. To facilitate bug fixing, data-driven models of automatic program repair use pairs of buggy and fixed code to learn transformations that fix errors in code. However, automatic repair of security vulnerabilities remains under-explored. In this work, we propose ways to improve code representations for vulnerability repair from three perspectives: input data type, data-driven models, and downstream tasks. The expected results of this work are improved code representations for automatic program repair and, specifically, fixing security vulnerabilities.


Accepted for the ICSE '22 Doctoral Symposium

Citation Keygrishina2022:ds-arxiv