Test Result Upload Automation

4/7/2026

Introduced the QMetry GitLab Component, a reusable CI/CD component available in the GitLab CI/CD Catalog. This component automates the upload of test execution data (JUnit XML, TestNG XML, etc.) from GitLab pipelines to the SmartBear QMetry test management platform. The post details the setup, configuration, and benefits of this integration, including eliminating manual uploads, enabling traceability, accelerating feedback, supporting compliance, and leveraging AI insights. It also covers obtaining QMetry API credentials and configuring GitLab CI/CD variables for secure storage.

12/14/2022

This post introduces Hurl, an open-source tool for testing web apps and APIs via HTTP requests, and demonstrates its integration into GitLab CI/CD pipelines. It covers installation, basic usage with plain text configurations, asserting HTTP responses (status, headers, body), and handling JSON/XML responses using JSONPath and XPath. The post details two primary methods for CI/CD integration: using the official Hurl container image with an entrypoint override, and building a custom container image for enhanced security and control. It also provides examples of creating reusable CI/CD job templates for efficient and dynamic testing.

11/1/2022

This 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.

7/5/2022

This post emphasizes the importance of modern software testing skills within a DevOps context. It highlights the benefits of test automation, advocates for incorporating manual testing fundamentals, outlines key principles of modern testing (e.g., business focus, fail fast, customer perspective, data-driven work), suggests pursuing certifications like ISTQB, and stresses the need to embrace new technologies like AI/ML for test automation. It also underscores the critical role of focusing on relevant metrics such as pipeline stability and time to first failure.

9/30/2021

This post highlights the challenges of manual testing and the benefits of integrating automated testing into the CI/CD pipeline, emphasizing the need for developers to receive scan results within their IDEs and pipelines to reduce context switching and improve efficiency. It also points to the value of a unified DevOps platform in streamlining the testing process and reducing deployment delays.

8/30/2019

This post discusses the challenges of software testing, specifically the lack of sufficient tests and the issue of flaky tests. It highlights GitLab's efforts to improve test coverage by emphasizing test requirements in issue and merge request templates. It also touches upon the need for test frameworks to be reliable and mimic manual tester behavior, likening user workflows to retrying dominoes. The post introduces the concept of using machine learning for test automation, envisioning a bot that can randomly interact with UI elements to find errors. Finally, it outlines goals to increase quality and productivity by adding more automated tests for enterprise features while shortening test runtime.

4/23/2018

This post introduces the concept of 'trust but verify' as a core principle for DevOps automation pipelines. It categorizes verification into Unit, System, and Compliance testing, advocating for their integration into the CI/CD pipeline to ensure code quality, system stability, and safety. The post argues that automated testing is crucial for maintaining trust in development teams and preventing the need for overly bureaucratic processes.