other · Lund University Publications Student Papers (Lund University) · 2023

Machine Learning Based Code Generation of Security Patches

Tsang, Ewada, Huang, Cecilia

Cited by 0Open access

Abstract

In this thesis, we explore the feasibility of using a machine learning model to generate security fixes for software vulnerabilities.Patching security bugs is crucial to ensure the safety of both individuals and organizations from malicious attacks, but it is also a time-consuming task.Despite recent advances in machine learning, automatic detection and fixing of security vulnerabilities are still largely unsolved problems.There is ongoing research in this field, which suggests to the potential of using machine learning as a tool to generate security fixes.However, these studies do not address the various variables that influence model performance.Therefore, we implemented a machine learning model to study how the dataset used to train it affects performance, aiming to understand important factors for practical use.Our observations on the impact of dataset size, lines changed, splitting method, weakness categories, and repository distributions provide valuable insights for developing a more generalizable model and performing data engineering on future datasets.

Cite this paper

APA 7

Tsang, E., & Huang, C. (2023). Machine Learning Based Code Generation of Security Patches. Lund University Publications Student Papers (Lund University).

MLA 9

Tsang, Ewada, and Cecilia Huang. “Machine Learning Based Code Generation of Security Patches.” Lund University Publications Student Papers (Lund University), 2023.

Chicago

Tsang, Ewada, and Cecilia Huang. 2023. “Machine Learning Based Code Generation of Security Patches.” Lund University Publications Student Papers (Lund University).

Harvard

Tsang, E. and Huang, C. (2023) ‘Machine Learning Based Code Generation of Security Patches’, Lund University Publications Student Papers (Lund University).

BibTeX

@misc{tsang2023machine,
  title = {{Machine Learning Based Code Generation of Security Patches}},
  author = {Tsang, Ewada and Huang, Cecilia},
  journal = {Lund University Publications Student Papers (Lund University)},
  year = {2023}
}

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