Amazon Web Services (AWS) detailed how development teams can streamline and modernize smoke testing within CI/CD pipelines by using Amazon Nova Act headless mode. With software teams frequently deploying updates several times per day, rapid validation of core application workflows has become critical to maintaining software quality and accelerating delivery cycles. Traditional, selector-based end-to-end tests often take hours to run and can slow the release cadence.
Smoke testing focuses on validating the most essential user journeys such as login, navigation, and key transactions to provide rapid verification of application health. Because these tests are lightweight and targeted, they complete in minutes rather than hours, making them ideal for continuous integration and delivery environments.
Amazon Nova Act, powered by artificial intelligence and natural language understanding, enables developers to automate UI testing without relying on fragile CSS selectors or complex test scripts. Instead, teams can write natural language commands that adapt to UI changes, significantly reducing test maintenance overhead.
This walkthrough showcases how to integrate Nova Act into CI/CD testing workflows with a demo using a sample ecommerce application, illustrating how to configure, run, and scale automated smoke tests.
Comprehensive Solution Overview
The solution outlined by AWS includes a Python-based test runner, ecommerce workflow smoke tests, GitLab CI/CD integration for automation, and parallel execution support for performance efficiency. Leveraging headless browser execution ensures tests can run silently in CI environments without visual interference, improving speed and resource utilization.
Key steps in the implementation include:
- Setting up the project environment
- Writing a basic smoke test for user login
- Extending tests to validate full ecommerce workflows
- Automating execution via GitLab CI/CD pipelines
- Enhancing efficiency with parallel test execution
Project Setup and Dependency Management
Developers are guided to initialize a new Python project using a modern package manager and install Nova Act dependencies. Automated environment configuration secures API keys through environment variables, and best practices for key rotation and monitoring are outlined.
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Smoke Test Creation and Workflow Validation
The first automated smoke test validates user login functionality using natural language commands, demonstrating how Nova Act interprets and interacts with application UI. Expanding this baseline, a more comprehensive smoke test simulates a full ecommerce journey from authentication and shopping cart actions to checkout and logout covering all mission-critical user interactions.
This layered approach ensures that the most impactful business workflows are tested at every deployment, limiting risk before committing changes to production.
CI/CD Integration with GitLab
Building on script-driven tests, AWS shows how to integrate smoke tests into GitLab CI/CD pipelines to ensure automated execution on every commit and merge. By enabling headless mode, tests run invisibly during automated pipeline runs, while still supporting visual debugging during local development.
Test steps within .gitlab-ci.yml include installing dependencies, setting headless mode, managing secure environment variables, and pushing commits to trigger pipelines automatically.
Parallel Execution for Faster Feedback
To further accelerate smoke test execution, AWS demonstrates adding parallel test execution using Python’s ThreadPoolExecutor. This approach runs multiple browser instances concurrently, significantly reducing total execution time a critical advantage for larger test suites and fast-paced CI/CD workflows.
Testing Best Practices and Production Readiness
The solution includes recommendations for maintaining test independence, balancing resource utilization, inserting retry logic for transient failures, and establishing schedules for daily environment validation. Secure API management and performance monitoring are also highlighted as key operational practices.
Conclusion
By leveraging Amazon Nova Act headless mode, organizations can transform manual and brittle testing practices into flexible, AI-powered automated smoke testing that integrates seamlessly with modern CI/CD pipelines. This solution reduces time-to-feedback, improves release confidence, and scales with development workflows.
