@misc{16585,
  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},
  journal = {44th International Conference on Software Engineering Companion (ICSE {\textquoteright}22 Companion), Doctoral Symposium},
  pages = {275-277},
  month = {05/2022},
  publisher = {Association for Computing Machinery},
  isbn = {978-1-4503-9223-5/22/05},
  doi = {10.1145/3510454.3517063},
}