CodeBench

Evaluate correctness and security for code-generating systems

Rigorous evaluation infrastructure for code-generating AI. Unit-test-backed correctness scoring, security red teaming, and code quality rubrics ensure your AI writes code that's correct, secure, and maintainable.

Comprehensive evaluation for AI code generation, including correctness and security.

Methodology: Our Code Evaluation Methodology

Comprehensive benchmarking for code generation and code understanding models. We test correctness, security, efficiency, and real-world applicability across languages.

  • Multi-language coverage
  • Security vulnerability checks
  • Performance benchmarking
  • Real-world use case testing

Modules & Capabilities

  • Unit-test-backed Correctness

    Automated tests and pass-rate scoring for generated code

  • Secure Coding Red Team

    Injection patterns, secret handling, and unsafe library detection

  • Code Review Rubrics

    Maintainability, clarity, and risk scoring frameworks

  • Tooling Evals

    CI/CD agent behavior, repo boundaries, and permission checks

Results: Reliable Code Generation

Code models evaluated with our benchmarks produce correct, secure, and efficient code that works in real-world development environments.

  • Higher pass rates
  • Security compliance
  • Performance optimization
  • Real-world applicability

Deliverables

  • Test suite + pass/fail dashboard
  • Vulnerability taxonomy and examples
  • Regression suite for model changes

Get started with CodeBench - contact our team for a scoping call.