Version updated for https://github.com/Quality-Max/qualitymax-github-action to version v1.3.0. This action is used across all versions by ? repositories. Action Type This is a Node action using Node version 20. Go to the GitHub Marketplace to find the latest changes. Action Summary The QualityMax Test Runner GitHub Action automates end-to-end (E2E) testing as part of your CI/CD pipeline, leveraging AI to generate, execute, and maintain tests. It provides fast feedback by running tests on every push, pull request, or schedule, with results automatically posted to pull requests for streamlined collaboration. Key features include AI-powered test creation, flaky test detection with automatic retries, local execution support, and the ability to configure test suites, browsers, and execution modes.

Version updated for https://github.com/Quality-Max/qualitymax-github-action to version v1.2.1. This action is used across all versions by ? repositories. Action Type This is a Node action using Node version 20. Go to the GitHub Marketplace to find the latest changes. Action Summary The QualityMax Test Runner GitHub Action automates AI-powered end-to-end (E2E) testing in CI/CD pipelines by executing tests on each code push, pull request, or scheduled run. It simplifies the testing process with features like automatic test generation and maintenance, flaky test detection with auto-retries, and PR comments summarizing test results. This action ensures faster feedback, seamless integration, and improved software quality with minimal configuration.

Version updated for https://github.com/Quality-Max/qualitymax-github-action to version v1.2.0. This action is used across all versions by ? repositories. Action Type This is a Node action using Node version 20. Go to the GitHub Marketplace to find the latest changes. Action Summary The QualityMax Test Runner GitHub Action integrates AI-powered end-to-end (E2E) testing into your CI/CD pipeline, automating test execution on every push, pull request, or scheduled event. It provides instant feedback by posting test results directly to pull requests, reducing manual testing efforts and enabling faster identification of issues. Key features include automated test generation and maintenance with AI, flaky test detection with auto-retries, local execution capability, and the ability to bootstrap tests via AI discovery.