AI-Generated Code Compliance
Why AI-Generated Code Requires Careful Auditing?
AI code generation assistants are degrading code quality:
- Coding standards are not consistently enforced.
- It increases duplicated code. AI is less likely to suggest reusing similar functions elsewhere in the code, partly due to limited context size.
- It reduces refactoring. AI still struggles to consolidate previous work into reusable and maintainable modules.
Ensuring Quality and Security in AI-Generated .NET Code with NDepend
NDepend enables developers to integrate AI-generated code while maintaining strict quality and security standards.
The tool executes NDepend rules, and consolidates Roslyn Analyzers, and Resharper code inspections results obtained on AI-generated .NET code.
The NDepend's Quality Gate strategy is then applied, ensuring strict code quality enforcement. A quality gate acts as an automated and objective goal for ensuring high code quality, whether for manually written or AI-generated code. Before deployment—and ideally before committing to source control—these quality goals must be met.
Common Quality Gates enforced by NDepend include:
- At least 80% code coverage achieved through test execution on legacy code, newly introduced code and refactored code. All thresholds are configurable.
- No issues with severity blocker nor critical .
- No newly introduced issues with severity high.
- No new compiler warnings.
Finally, integrating NDepend into your CI/CD pipeline is essential. With Web Report generation, the Azure DevOps extension, or the GitHub Action, you can continuously enforce rules and validate quality gates for both manually written and AI-generated code.