AuthorsA. Grishina
TitleEnabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods
AfilliationSoftware Engineering
Project(s)Data-Driven Software Engineering Department
Publication TypeProceedings, refereed
Year of Publication2022
Conference Name44th International Conference on Software Engineering Companion (ICSE ’22 Companion), Doctoral Symposium
Date Published05/2022
PublisherAssociation for Computing Machinery
ISBN Number978-1-4503-9223-5/22/05
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.

Citation Key42490