mlsanityData to 7 Oct 2026

Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods

arXiv 2202.03055 ↗First posted 7 Feb 2022cs.SE cs.CR cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 7 Feb 2022.

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20 of 38 references.

References · 20

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8 more
  1. 2018An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translationcited by 106
  2. 2021CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Softwarecited by 89
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  5. 2018Automatic Software Repair: a Bibliographycited by 24
  6. 2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 23
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  8. 2020Recommending Stack Overflow Posts for Fixing Runtime Exceptions using Failure Scenario Matchingcited by 9

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024A Survey of Large Language Models in Cybersecuritycited by 8