@misc{16510,
  keywords = {software security, static analysis, automatic program repair, natural language processing, graph-based machine learning, ml4code},
  author = {Anastasiia Grishina},
  title = {Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods},
  abstract = {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.},
  year = {2022},
  pages = {3 pages},
  month = {02/2022},
  publisher = {arXiv},
  url = {http://arxiv.org/abs/2202.03055},
  note = {Accepted for the ICSE {\textquoteright}22 Doctoral Symposium},
}