Adding Tests to Your Warp AI Project

Most Warp AI projects ship with zero tests. This means every change is a gamble — you might fix one thing and break three others without knowing until a user reports it. Tests are automated checks that verify your app works correctly. They run in seconds and catch bugs before your users do.

Test generated shell scripts in a clean, isolated environment before running on production systems. Use shellcheck to lint shell scripts generated by Warp. For Kubernetes and Terraform configs, use their respective validation tools (kubectl validate, terraform validate) before applying

Test shell scripts with edge cases: empty inputs, files with spaces in names, commands that fail mid-script. Test infrastructure configs in a non-production environment. Verify that generated cleanup scripts remove what they should and only what they should

Unit tests check individual functions in isolation — does this function calculate the right total? Integration tests check that multiple pieces work together — does submitting this form save to the database? End-to-end (E2E) tests simulate a real user — can someone sign up, log in, and complete a purchase? Start with E2E tests for your most critical flows, then add integration tests for your API endpoints.

Set up your tests to run automatically on every code push using GitHub Actions or a similar CI service. This way, you'll know immediately if a change breaks something. A basic CI pipeline runs your tests, checks for TypeScript errors, and builds the project. If any step fails, the push is flagged before it reaches production.