Automated source code generation is currently a popular machine-learning-based task. It
can be helpful for software developers to write functionally correct code from a given context …

Abstract Machine learning methods are widely used to identify common, repeatedly
occurring bugs and code vulnerabilities. The performance of a machine-learned model is …

As the role of information and communication technologies gradually increases in our lives,
software security becomes a major issue to provide protection against malicious attempts …

Vulnerable source code in software applications is causing paramount reliability and
security issues. Software security principles should be integrated to reduce these issues at …

We review machine learning approaches for detecting (and correcting) vulnerabilities in
source code, finding that the biggest challenges ahead involve agreeing to a benchmark …

Context: Identifying potential vulnerable code is important to improve the security of our
software systems. However, the manual detection of software vulnerabilities requires expert …

Modern code generation tools use AI models, particularly Large Language Models (LLMs),
to generate functional and complete code. While such tools are becoming popular and …

Several advances in deep learning have been successfully applied to the software
development process. Of recent interest is the use of neural language models to build tools …

Large Language Models (LLMs) like Codex are powerful tools for performing code
completion and code generation tasks as they are trained on billions of lines of code from …

In recent years, the use of automated source code generation utilizing transformer-based
generative models has expanded, and these models can generate functional code …