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Test Result Upload Automation

7 posts · Apr 2018 – Apr 2026

What this trail covers

GitLab CI/CD capabilities are enhanced with automated test result uploads from pipelines to external test management platforms like QMetry, enabling end-to-end traceability, faster feedback loops, and compliance adherence. This builds upon existing CI/CD features for running tests and generating reports. The integration of Hurl allows for continuous testing of web apps and APIs directly within GitLab CI/CD jobs, enabling automated HTTP request testing, response assertion, and integration with cu

All GitLab trails
  1. Streamline test management with SmartBear QMetry GitLab componentThis post details the integration of a new QMetry GitLab Component into CI/CD pipelines for automated test result uploads. It covers obtaining QMetry API credentials, configuring GitLab CI/CD variables for secure storage, and verifying test output formats (JUnit XML). The component's functionality is explained, highlighting benefits like eliminating manual uploads, enabling end-to-end traceability, accelerating feedback loops, and supporting compliance. It also touches on the partnership between GitLab and SmartBear.
  2. How to continuously test web apps and APIs with Hurl and GitLab CI/CDThis post introduces Hurl, an open-source tool for testing web apps and APIs using plain text configurations. It details Hurl's installation, basic usage with GET requests and assertions on HTTP protocol, status, headers, and body. It also covers advanced use cases like JSONPath and XPath assertions, chaining requests, and integrating Hurl into GitLab CI/CD pipelines using official container images or custom-built images. The post provides examples of CI/CD job templates for efficient testing and monitoring.
  3. How to automate testing for a React application with GitLabThis post details how to configure GitLab CI/CD to automate the testing of a React application. It covers setting up a React app with Create React App, running Jest tests locally, and configuring a `.gitlab-ci.yml` file to execute tests in a CI pipeline. Key technical details include using `jest-junit` to generate JUnit reports, configuring the `test:ci` script in `package.json` for CI environments with coverage collection, and defining a CI job to run tests, upload coverage artifacts, and report JUnit results.
  4. Want faster releases? Your answer lies in automated software testingThis post highlights the ongoing challenges and frustrations with integrating automated software testing into the DevOps lifecycle, emphasizing the need for developers to receive test results within their IDEs and within the flow of their work. It proposes more automation, AI/ML, and an integrated DevOps platform as solutions. It also references the benefits experienced by Glympse after moving to GitLab's DevOps Platform, which streamlined deployments and reduced testing fatigue.
  5. An inside look at software testing at GitLabThis post discusses the challenges of software testing at GitLab, including the lack of sufficient tests and flaky tests. It highlights efforts to improve test requirements by including them in issue and merge request templates. The post also emphasizes the importance of test framework reliability and the need for developers to contribute to end-to-end testing. It touches on the potential of machine learning to assist in test automation by creating bots that can identify UI locators and randomly click on them to find errors. The team aims to increase test coverage for enterprise features while reducing test runtime to 30 minutes.
  7. Trust, but verify: The importance of software test automationThis post emphasizes the critical role of automated testing (Unit, System, Compliance, Security) within a DevOps pipeline as a verification mechanism to complement trust in development teams. It advocates for integrating these tests into the CI/CD pipeline to ensure system stability, customer needs representation, and software safety, thereby reducing the need for slow, manual processes and change advisory boards.