AI Code Assurance
Ensure the quality and security of every line of AI generated code by instilling confidence using our code assurance workflow.
USED AND LOVED BY 7 MILLION DEVELOPERS & 400,000+Organizations
AI Code Assurance in SonarQube and SonarCloud empowers developers and organizations to confidently integrate AI into their coding. By enforcing high standards of quality and security, it guides developers through a thorough validation process, ensuring AI generated code is fully understood and verified before reaching production.
Sonar AI Code Assurance is a robust and streamlined process for validating AI-generated code through a structured and comprehensive analysis. This ensures that every new piece of code meets the highest standards of quality and security before it moves to production.
Developers easily identify and tag projects that contain AI-generated code. This simple step initiates the Sonar AI Code Assurance workflow, a thorough analysis and validation process that guides developers though the resolution of issues and unique challenges posed by AI-generated content.
Empowers developers to take ownership of all code, ensuring that every piece of AI-generated content is thoroughly analyzed and reviewed.
Teams can easily identify and track the status of projects containing AI-generated code, providing comprehensive visibility of its quality and security.
Integrates natively within existing workflows, ensuring that developers can continue to work efficiently without added overhead.
Helps the organization eliminate risk and develop confidence in AI, ultimately driving wider and safer adoption of the technology.
"Sonar helps our development team confidently make both AI-assisted and human-developed code fit for production by reviewing and establishing rules of good programming practices to achieve better code and avoid typical errors."
Dario Flores, Technical Quality Specialist
Dario Flores, Technical Quality Specialist
"Sonar helps our development team confidently make both AI-assisted and human-developed code fit for production by reviewing and establishing rules of good programming practices to achieve better code and avoid typical errors."
