Analysis of Datasets and Large Language Models for Vulnerability Detection in Imperative Programming Language Code

Large language models are machine learning models that enable the classification and generation of both natural language text and code in various programming languages. These models have billions of parameters and are trained on vast datasets. In recent years, such models have been successfully applied to a wide range of tasks in software engineering. The paper presents data on publication activity on the topic under study, which is obtained on the basis of statistical analysis of search results for relevant key queries. In addition, a review of recent publications in the field of using large language models to detect vulnerabilities in program code is carried out, and the results of the analysis of data sets that are used in training neural network models to search for vulnerabilities in program code are presented.