[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/from-code-to-production-a-guide-to-continuous-deployment-with-gitlab":3,"navigation-en-us":42,"banner-en-us":451,"footer-en-us":461,"blog-post-authors-en-us-Benjamin Skierlak|James Wormwell":702,"blog-related-posts-en-us-from-code-to-production-a-guide-to-continuous-deployment-with-gitlab":728,"assessment-promotions-en-us":765,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":29,"isFeatured":13,"meta":30,"navigation":31,"path":32,"publishedDate":22,"seo":33,"stem":37,"tagSlugs":38,"__hash__":41},"blogPosts/en-us/blog/from-code-to-production-a-guide-to-continuous-deployment-with-gitlab.yml","From Code To Production A Guide To Continuous Deployment With Gitlab",[7,8],"benjamin-skierlak","james-wormwell",null,"product",{"slug":12,"featured":13,"template":14},"from-code-to-production-a-guide-to-continuous-deployment-with-gitlab",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"From code to production: A guide to continuous deployment with GitLab","Learn how to get started building a robust continuous deployment pipeline in GitLab. Follow these step-by-step instructions, practical examples, and best practices.",[19,20],"Benjamin Skierlak","James Wormwell","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659478/Blog/Hero%20Images/REFERENCE_-_Use_this_page_as_a_reference_for_thumbnail_sizes.png","2025-01-28","Continuous deployment is a game-changing practice that enables teams to deliver value faster, with higher confidence. However, diving into advanced deployment workflows — such as GitOps, container orchestration with Kubernetes, or dynamic environments — can be intimidating for teams just starting out.\n\nAt GitLab, we're committed to making delivery seamless and scalable. By enabling teams to focus on the fundamentals, we empower them to build a strong foundation that supports growth into more complex strategies over time. This guide provides essential steps to begin implementing continuous deployment with GitLab, laying the foundation for your long-term success.\n\n## Start with a workflow plan\n\nBefore diving into the technical implementation, take time to map out your deployment workflow. Success lies in careful planning and a methodical approach.\n\n### Artifact management strategy\n\nIn the context of continuous deployment, artifacts are the packaged outputs of your build process that need to be stored, versioned, and deployed. These could be:\n\n- container images for your applications\n- packages\n- compiled binaries or executables\n- libraries\n- configuration files\n- documentation packages\n- other artifacts\n\nEach type of artifact plays a specific role in your deployment process. For example, a typical web application might generate:\n\n- a container image for the backend service\n- a ZIP archive of compiled frontend assets\n- SQL files for database changes\n- environment-specific configuration files\n\nManaging these artifacts effectively is crucial for successful deployments. Here's how to approach artifact management.\n\n#### Artifacts and releases versioning strategies\n\nA best practice to get you started with a clean structure is to establish a clear versioning strategy for your artifacts. When creating releases:\n\n- Use semantic versioning (major.minor.patch) for release tags\n  - Example: `myapp:1.2.3` for a stable release\n  - Major version changes (2.0.0) for breaking changes\n  - Minor version changes (1.3.0) for new features\n  - Patch version changes (1.2.4) for bug fixes\n- Maintain a 'latest' tag for the most recent stable version\n  - Example: `myapp:latest` for automated deployments\n- Include commit SHA for precise version tracking\n  - Example: `myapp:1.2.3-abc123f` for debugging\n- Consider branch-based tags for development environments\n  - Example: `myapp:feature-user-auth` for feature testing\n\n#### Build artifacts retention\n\nImplement defined retention rules:\n\n- Set explicit expiration timeframes for temporary artifacts\n- Define which artifacts need permanent retention\n- Configure cleanup policies to manage storage\n\n#### Registry access and authentication\n\nSecure your artifacts with proper access controls:\n\n- Implement Personal Access Tokens for developer access\n- Configure CI/CD variables for pipeline authentication\n- Set up proper access scopes\n\n### Environment strategy\n\nConsider your environments early, as they shape your entire deployment pipeline:\n\n- Development, staging, and production environment configurations\n- Environment-specific variables and secrets\n- Access controls and protection rules\n- Deployment tracking and monitoring approach\n\n### Deployment targets\n\nBe intentional as to where and how you'll deploy, these decisions matter and the benefits and drawbacks of each should be consider:\n\n- Infrastructure requirements (VMs, containers, cloud services)\n- Network access and security configurations\n- Authentication mechanisms (SSH keys, access tokens)\n- Resource allocation and scaling considerations\n\nWith our strategy defined and foundational decisions made, we can now translate these plans into a working pipeline. We'll build a practical example that demonstrates these concepts, starting with a simple application and progressively adding deployment capabilities.\n\n## Implementing your CD pipeline\n\n### A step-by-step example\n\nLet's walk through implementing a basic continuous deployment pipeline for a web application. We'll use a simple HTML application as an example, but these principles apply to any type of application. We’re also going to deploy our application as a Docker image on a simple virtual machine. This will allow us to lean on a curated image with minimum dependencies, and to ensure no environment specific requirements are unintentionally brought in. By working on a virtual machine, we won’t be leveraging GitLab’s native integrations, allowing us to work on an easier but less scalable setup to begin with.\n\n#### Prerequisites\n\nIn this example, we’ll aim to containerize an application that we’ll run on a virtual machine hosted on a cloud provider. We’ll also test this application locally on our machine. This list of prerequisites is only needed for this scenario.\n\n##### Virtual machine setup\n\n- Provision a VM in your preferred cloud provider (e.g., GCP, AWS, Azure)\n- Configure network rules to allow access on ports 22, 80, and 443\n- Record the machine's public IP address for deployment\n\n##### Set up SSH authentication:\n\n- Generate a public/private key pair for the machine\n- In GitLab, go to **Settings > CI/CD > Variables**\n- Create a variable called `GITLAB_KEY`\n- Set Type to \"File\" (required for SSH authentication)\n- Paste the private key in the Value field\n- Define a USER variable, this is the user logging in and running the scripts on your VM\n\n##### Configure deployment variables\n\n- Create variables for your deployment targets:\n  - `STAGING_TARGET`: Your staging server IP/domain\n  - `PRODUCTION_TARGET`: Your production server IP/domain\n\n##### Local development setup\n\n- Install Docker on your local machine for testing deployments\n\n##### GitLab Container Registry access\n\n- Locate your registry path:\n  - Navigate to **Deploy > Container Registry**\n  - Copy the registry path (e.g., registry.gitlab.com/group/project)\n- Set up authentication:\n  - Go to **Settings > Access Tokens**\n  - Create a new token with registry access\n  - Token expiration: Maximum 1 year\n  - Save the token securely\n- Configure local registry access:\n\n```shell\ndocker login registry.gitlab.com\n# The username if you are using a PAT is gitlab-ci-token\n# Password: your-access-token\n```\n\n#### 1. Create your application\n\nStart with a basic web application. For our example, we're using a simple HTML page:\n\n```xml\n\u003C!-- index.html -->\n\u003Chtml>\n  \u003Chead>\n    \u003Cstyle>\n      body {\n        background-color: #171321; /* GitLab dark */\n      }\n    \u003C/style>\n  \u003C/head>\n  \u003Cbody>\n    \u003C!-- Your content here -->\n  \u003C/body>\n\u003C/html>\n```\n\n#### 2. Containerize your application\n\nCreate a Dockerfile to package your application:\n\n```text\nFROM nginx:1.26.2\nCOPY index.html /usr/share/nginx/html/index.html\n```\n\nThis Dockerfile:\n\n- Uses nginx as a base image for serving web content\n- Copies your HTML file to the correct location in the nginx directory structure\n\n#### 3. Set up your CI/CD pipeline\n\nCreate a `.gitlab-ci.yml` file to define your pipeline stages:\n\n```yaml\nvariables:\n  TAG_LATEST: $CI_REGISTRY_IMAGE/$CI_COMMIT_REF_NAME:latest\n  TAG_COMMIT: $CI_REGISTRY_IMAGE/$CI_COMMIT_REF_NAME:$CI_COMMIT_SHA\n\nstages:\n  - publish\n  - deploy\n\n```\n\nLet's break it down:\n\n`TAG_LATEST` is made up of three parts:\n\n- `$CI_REGISTRY_IMAGE` is the path to your project's container registry in GitLab\n\nFor example: `registry.gitlab.com/your-group/your-project`\n\n- `$CI_COMMIT_REF_NAME` is the name of your branch or tag\n\nFor example, if you're on main branch: `/main`, and if you're on a feature branch: `/feature-login`\n\n- `:latest` is a fixed suffix\n\nSo if you're on the main branch, `TAG_LATEST` becomes: `registry.gitlab.com/your-group/your-project/main:latest`.\n\n`TAG_COMMIT` is almost identical, but instead of `:latest`, it uses: `$CI_COMMIT_SHA` which is the commit identifier, for example: `:abc123def456`.\n\nSo for that same commit on main branch, `TAG_COMMIT` becomes:` registry.gitlab.com/your-group/your-project/main:abc123def456`.\n\nThe reason for having both is `TAG_LATEST` gives you an easy way to always get the newest version, and `TAG_COMMIT` gives you a specific version you can return to if needed.\n\n#### 4. Publish to the container registry\n\nAdd the publish job to your pipeline:\n\n```yaml\npublish:\n  stage: publish\n  image: docker:latest\n  services:\n    - docker:dind\n  script:\n    - docker build -t $TAG_LATEST -t $TAG_COMMIT .\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - docker push $TAG_LATEST\n    - docker push $TAG_COMMIT\n\n```\n\nThis job:\n\n- Uses Docker-in-Docker to build images\n- Creates two tagged versions of your image\n- Authenticates with the GitLab registry\n- Pushes both versions to the registry \n\nNow that our images are safely stored in the registry, we can focus on deploying them to our target environments. Let's start with local testing to validate our setup before moving to production deployments.\n\n#### 5. Deploy to your environment\n\nBefore deploying to production, you can test locally. We just published our image to the GitLab repository, which we’ll pull locally. If you’re unsure of the exact path, navigate to **Deploy > Container Registry**, and you should see an icon to copy the path of your image at the end of the line for the container image you want to test.\n\n```shell\ndocker login registry.gitlab.com \ndocker run -p 80:80 registry.gitlab.com/your-project-path/main:latest\n```\n\nBy doing so you should be able to access your application locally on your localhost address through your web browser.\n\nYou can now add a deployment job to your pipeline:\n\n```yaml\ndeploy:\n  stage: deploy\n  image: alpine:latest\n  script:\n    - chmod 400 $GITLAB_KEY\n    - apk add openssh-client\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - ssh -i $GITLAB_KEY -o StrictHostKeyChecking=no $USER@$TARGET_SERVER \n      docker pull $TAG_COMMIT &&\n      docker rm -f myapp || true &&\n      docker run -d -p 80:80 --name myapp $TAG_COMMIT\n\n```\n\nThis job:\n\n- Sets up SSH access to your deployment target\n- Pulls the latest image\n- Removes any existing container\n- Deploys the new version\n\n#### 6. Track deployments\n\nEnable deployment tracking by adding environment configuration:\n\n```yaml\ndeploy:\n  environment:\n    name: production\n    url: https://your-application-url.com \n\n```\n\nThis creates an environment object in GitLab's **Operate > Environments** section, providing:\n\n- Deployment history\n- Current deployment status\n- Quick access to your application\n\nWhile a single environment pipeline is a good starting point, most teams need to manage multiple environments for proper testing and staging. Let's expand our pipeline to handle this more realistic scenario.\n\n#### 7. Set up multiple environments\n\nFor a more robust pipeline, configure staging and production deployments:\n\n```yaml\nstages:\n  - publish\n  - staging\n  - release\n  - version\n  - production\n\nstaging:\n  stage: staging\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\" && $CI_COMMIT_TAG == null\n  environment:\n    name: staging\n    url: https://staging.your-app.com\n  # deployment script here\n\nproduction:\n  stage: production\n  rules:\n    - if: $CI_COMMIT_TAG\n  environment:\n    name: production\n    url: https://your-app.com\n  # deployment script here\n\n```\n\nThis setup:\n\n- Deploys to staging from your main branch\n- Uses GitLab tags to trigger production deployments\n- Provides separate tracking for each environment\n\nHere and in our next step, we’re leveraging a very useful GitLab feature: tags. By manually creating a tag in the **Code > Tags** section, the `$CI_COMMIT_TAG` gets created, which allows us to trigger jobs accordingly.\n\n#### 8. Create automated release notes\n\nWe'll be using GitLab's release capabilities through our CI/CD pipeline. First, update your stages in `.gitlab-ci.yml`:\n\n```yaml\nstages:\n\n- publish\n- staging\n- release # New stage for releases\n- version\n- production\n```\n\nNext, add the release job:\n\n```yaml\nrelease_job:\n  stage: release\n  image: registry.gitlab.com/gitlab-org/release-cli:latest\n  rules:\n    - if: $CI_COMMIT_TAG                  # Only run when a tag is created\n  script:\n    - echo \"Creating release for $CI_COMMIT_TAG\"\n  release:                                # Release configuration\n    name: 'Release $CI_COMMIT_TAG'\n    description: 'Release created from $CI_COMMIT_TAG'\n    tag_name: '$CI_COMMIT_TAG'           # The tag to create\n    ref: '$CI_COMMIT_TAG'                # The tag to base release on\n\n```\n\nYou can enhance this by adding links to your container images:\n\n```yaml\nrelease:\n  name: 'Release $CI_COMMIT_TAG'\n  description: 'Release created from $CI_COMMIT_TAG'\n  tag_name: '$CI_COMMIT_TAG'\n  ref: '$CI_COMMIT_TAG'\n  assets:\n    links:\n      - name: 'Container Image'\n        url: '$CI_REGISTRY_IMAGE/main:$CI_COMMIT_TAG'\n        link_type: 'image'\n\n```\n\nFor meaningful automated release notes:\n\n- Use conventional commits (feat:, fix:, etc.)\n- Include issue numbers (#123)\n- Separate subject from body with blank line\n\nIf you want custom release notes with deployment info:\n\n```text\nrelease_job:\n  script:\n    - |\n      DEPLOY_TIME=$(date '+%Y-%m-%d %H:%M:%S')\n      CHANGES=$(git log $(git describe --tags --abbrev=0 @^)..@ --pretty=format:\"- %s\")\n      cat > release_notes.md \u003C\u003C EOF\n      ## Deployment Info\n      - Deployed on: $DEPLOY_TIME\n      - Environment: Production\n      - Version: $CI_COMMIT_TAG\n\n      ## Changes\n      $CHANGES\n\n      ## Artifacts\n      - Container Image: \\`$CI_REGISTRY_IMAGE/main:$CI_COMMIT_TAG\\`\n      EOF\n  release:\n    description: './release_notes.md'\n\n```\n\nOnce configured, releases will be created automatically when you create a Git tag. You can view them in GitLab under **Deploy > Releases**.\n\n#### 9. Put it all together\n\nThis is what our final YAML file looks like:\n\n```text\nvariables:\n  TAG_LATEST: $CI_REGISTRY_IMAGE/$CI_COMMIT_REF_NAME:latest\n  TAG_COMMIT: $CI_REGISTRY_IMAGE/$CI_COMMIT_REF_NAME:$CI_COMMIT_SHA\n  STAGING_TARGET: $STAGING_TARGET    # Set in CI/CD Variables\n  PRODUCTION_TARGET: $PRODUCTION_TARGET  # Set in CI/CD Variables\n\nstages:\n  - publish\n  - staging\n  - release\n  - version\n  - production\n\n# Build and publish to registry\npublish:\n  stage: publish\n  image: docker:latest\n  services:\n    - docker:dind\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\" && $CI_COMMIT_TAG == null\n  script:\n    - docker build -t $TAG_LATEST -t $TAG_COMMIT .\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - docker push $TAG_LATEST\n    - docker push $TAG_COMMIT\n\n# Deploy to staging\nstaging:\n  stage: staging\n  image: alpine:latest\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\" && $CI_COMMIT_TAG == null\n  script:\n    - chmod 400 $GITLAB_KEY\n    - apk add openssh-client\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - ssh -i $GITLAB_KEY -o StrictHostKeyChecking=no $USER@$STAGING_TARGET \"\n        docker pull $TAG_COMMIT &&\n        docker rm -f myapp || true &&\n        docker run -d -p 80:80 --name myapp $TAG_COMMIT\"\n  environment:\n    name: staging\n    url: http://$STAGING_TARGET\n\n# Create release\nrelease_job:\n  stage: release\n  image: registry.gitlab.com/gitlab-org/release-cli:latest\n  rules:\n    - if: $CI_COMMIT_TAG\n  script:\n    - |\n      DEPLOY_TIME=$(date '+%Y-%m-%d %H:%M:%S')\n      CHANGES=$(git log $(git describe --tags --abbrev=0 @^)..@ --pretty=format:\"- %s\")\n      cat > release_notes.md \u003C\u003C EOF\n      ## Deployment Info\n      - Deployed on: $DEPLOY_TIME\n      - Environment: Production\n      - Version: $CI_COMMIT_TAG\n\n      ## Changes\n      $CHANGES\n\n      ## Artifacts\n      - Container Image: \\`$CI_REGISTRY_IMAGE/main:$CI_COMMIT_TAG\\`\n      EOF\n  release:\n    name: 'Release $CI_COMMIT_TAG'\n    description: './release_notes.md'\n    tag_name: '$CI_COMMIT_TAG'\n    ref: '$CI_COMMIT_TAG'\n    assets:\n      links:\n        - name: 'Container Image'\n          url: '$CI_REGISTRY_IMAGE/main:$CI_COMMIT_TAG'\n          link_type: 'image'\n\n# Version the image with release tag\nversion_job:\n  stage: version\n  image: docker:latest\n  services:\n    - docker:dind\n  rules:\n    - if: $CI_COMMIT_TAG\n  script:\n    - docker pull $TAG_COMMIT\n    - docker tag $TAG_COMMIT $CI_REGISTRY_IMAGE/main:$CI_COMMIT_TAG\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - docker push $CI_REGISTRY_IMAGE/main:$CI_COMMIT_TAG\n\n# Deploy to production\nproduction:\n  stage: production\n  image: alpine:latest\n  rules:\n    - if: $CI_COMMIT_TAG\n  script:\n    - chmod 400 $GITLAB_KEY\n    - apk add openssh-client\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n    - ssh -i $GITLAB_KEY -o StrictHostKeyChecking=no $USER@$PRODUCTION_TARGET \"\n        docker pull $CI_REGISTRY_IMAGE/main:$CI_COMMIT_TAG &&\n        docker rm -f myapp || true &&\n        docker run -d -p 80:80 --name myapp $CI_REGISTRY_IMAGE/main:$CI_COMMIT_TAG\"\n  environment:\n    name: production\n    url: http://$PRODUCTION_TARGET\n\n```\n\nThis complete pipeline:\n\n- Publishes images to the registry (main branch)\n- Deploys to staging (main branch)\n- Creates releases (on tags)\n- Versions images with release tags\n- Deploys to production (on tags)\n\nKey benefits:\n\n- Clean reproducible, local development and testing environment\n- Clear path to production environments with structure to build confidence in what is deployed\n- Pattern to recover from unexpected failures, etc.\n- Ready to scale/adopt more complex deployment strategies\n\n### Best practices\n\nThroughout implementation, maintain these principles:\n\n- Document everything, from variable usage to deployment procedures\n- Use GitLab's built-in features (environments, releases, registry)\n- Implement proper access controls and security measures\n- Plan for failure with robust rollback procedures\n- Keep your pipeline configurations DRY (Don't Repeat Yourself)\n\n## Scale your deployment strategy\n\nWhat next? Here are some aspects to consider as your continuous deployment strategy matures.\n\n### Advanced security measures\n\nEnhance security through:\n\n- Protected environments with restricted access\n- Required approvals for production deployments\n- Integrated security scanning\n- Automated vulnerability assessments\n- Branch protection rules for deployment-related changes\n\n### Progressive delivery strategies\n\nImplement advanced deployment strategies:\n\n- Feature flags for controlled rollouts\n- Canary deployments for risk mitigation\n- Blue-green deployment strategies\n- A/B testing capabilities\n- Dynamic environment management\n\n### Monitoring and optimization\n\nEstablish robust monitoring practices:\n\n- Track deployment metrics\n- Set up performance monitoring\n- Configure deployment alerts\n- Establish deployment SLOs\n- Regular pipeline optimization\n\n## Why GitLab?\n\nGitLab's continuous deployment capabilities make it a standout choice for modern deployment workflows. The platform excels in streamlining the path from code to production, offering built-in container registry, environment management, and deployment tracking all within a single interface. GitLab's environment-specific variables, deployment approval gates, and rollback capabilities provide the security and control needed for production deployments, while features like review apps and feature flags enable progressive delivery approaches. As part of GitLab's complete DevSecOps platform, these CD capabilities seamlessly integrate with your entire software lifecycle.\n\n## Get started today\n\nThe journey to continuous deployment is an evolution, not a revolution. Start with the fundamentals, build a solid foundation, and gradually incorporate advanced features as your team's needs grow. GitLab provides the tools and flexibility to support you at every stage of this journey, from your first automated deployment to complex, multi-environment delivery pipelines.\n\n> Sign up for a [free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/devsecops/) to get started with continous deployment 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DevSecOps teams need seamless integration between their CI/CD pipelines and test management platforms to maintain visibility, traceability, and compliance across the software development lifecycle.\n\nThis becomes even more important as testing scales across automated pipelines, where execution data is spread across tools and harder to track in one place.\n\nFor organizations using GitLab for CI/CD and SmartBear QMetry for test management, manually uploading test results creates friction, delays feedback loops, and makes it harder to maintain a reliable, centralized view of testing.\n\nWhat if you could automatically publish your JUnit, TestNG, or other test results directly from your GitLab pipeline to QMetry with just a few lines of configuration?\n\nThat's exactly what the new **QMetry GitLab Component** enables. This reusable CI/CD component, now available in the [GitLab CI/CD Catalog](https://gitlab.com/explore/catalog), eliminates the manual overhead of test result management by automatically uploading test execution data to QMetry.  This is an AI-enabled, enterprise-grade test management platform that brings together test planning, execution, tracking, and reporting in one place.\n\nAs a centralized system of record for testing, QMetry helps teams understand coverage, track execution, and make more reliable release decisions.\n\nIn this guide, you'll learn:\n\n* How to set up the QMetry GitLab Component in your pipeline  \n* How to configure automated test result uploads  \n* Advanced configuration options for enterprise requirements  \n* A real-world aerospace industry use case  \n* Best practices for test management automation\n\nBy the end of this article, your GitLab pipelines will automatically feed test results into QMetry, giving your QA teams instant visibility into test execution and helping them make faster, more confident release decisions.\n\n![SmartBear QMetry GitLab integration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775488045/ojt707rzxnm2yr3vqxdh.png)\n\n## Why integrate GitLab with QMetry?\n\nBefore diving into the technical implementation, let's understand the value this integration delivers:\n\n### Eliminate manual test result uploads\n\nDevSecOps engineers and QA teams no longer need to manually export test results from CI/CD runs and import them into test management systems. The component handles this automatically after every pipeline execution.\n\nThis reduces manual effort while ensuring test data stays consistent, up to date, and easy to access across teams.\n\n![Test results with SmartBear QMetry GitLab integration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775488045/ajx64sihup2nursdpnxz.png)\n\n### Enable end-to-end traceability\n\nBy connecting GitLab's CI/CD execution data with QMetry's test management capabilities, teams gain complete traceability from requirements through test cases to actual test execution results. This is critical for regulated industries like financial services, aerospace, medical devices, and automotive, where audit trails are mandatory and regulatory compliance depends on demonstrating complete test coverage.\n\nIt also gives teams a clearer view of coverage and risk across releases, making it easier to understand what’s been tested and what still needs attention.\n\n### Accelerate feedback loops\n\nAutomated test result uploads mean QA teams, product managers, and stakeholders see test execution results immediately after pipeline completion – no waiting for manual data entry or report generation.\n\nWith faster access to results, teams can act immediately, reduce delays, and make quicker, more informed release decisions.\n\n### Support compliance and audit requirements\n\nFor organizations in regulated industries, maintaining comprehensive test records with proper versioning and traceability is non-negotiable. This integration ensures you can document every test execution properly in QMetry with links back to the specific GitLab pipeline, commit, and build.\n\nThis creates an audit-ready record of testing activity without adding manual overhead.\n\n![Audit-ready record of testing with SmartBear QMetry GitLab integration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775488045/q2tbaw5otgdywjkcquqx.png)\n\n### Leverage AI-powered test insights\n\nQMetry uses AI to analyze test execution patterns, identify flaky tests, predict test failures, and recommend optimization opportunities. Feeding it real-time data from GitLab pipelines maximizes the value of these AI capabilities.\n\nWith continuous data flowing in, teams get more accurate insights and can focus their efforts where it matters most.\n\n![Accurage insights with SmartBear QMetry GitLab integration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775488045/pl7ru4wx8ixnheedfyrs.png)\n\n## About the GitLab and SmartBear partnership\n\nThis component represents a growing partnership between GitLab and SmartBear to better connect CI/CD execution with test management in a single workflow. SmartBear brings deep expertise in testing, API management, and quality automation, while GitLab provides the most comprehensive AI-powered DevSecOps platform. Together, they help teams streamline how testing fits into the development lifecycle while maintaining the quality, security, and compliance standards their industries require.\n\nWhether you're managing test execution for aerospace flight control systems, financial services platforms, automotive safety applications, or medical device software, the combination of GitLab's CI/CD capabilities and QMetry's test management gives teams a centralized, reliable view of testing across the lifecycle, helping them track execution, maintain traceability, and make more confident release decisions.\n\n## What you'll need\n\nBefore getting started, ensure you have:\n\n* **A GitLab account** with a project containing automated tests that generate test result files (JUnit XML, TestNG XML, etc.)  \n* **QMetry Test Management Enterprise** account with API access enabled  \n* **QMetry API Key** generated  from your QMetry instance (we'll cover this shortly)  \n* **QMetry Project** already created where you will upload test results   \n* **Familiarity with GitLab CI/CD**, including understanding of basic `.gitlab-ci.yml` syntax and pipeline concepts  \n* **Test suite configuration** in QMetry (optional but recommended for better organization)\n\n### Understanding the test result flow\n\nHere's what happens when you integrate this component:\n\n1. **Test execution**: Your GitLab CI/CD pipeline runs automated tests (unit tests, integration tests, E2E tests, etc.).  \n2. **Result generation**: Tests produce output files in formats like JUnit XML, TestNG XML, or other supported formats.  \n3. **Component invocation**: The QMetry component executes as a job in your pipeline.  \n4. **Automatic upload**: The component reads your test result files and uploads them to QMetry via API.  \n5. **QMetry processing**: QMetry receives the results, processes them, and makes them available for reporting and analysis.\n\nThe beauty of this integration is that it happens automatically, with no manual intervention required once configured.\n\n## Part 1: Getting your QMetry API credentials\n\nBefore configuring the GitLab component, you need to obtain API access credentials from your QMetry instance. Here are the steps to follow:\n\n### 1. Access QMetry settings\n\n1. Log in to your **QMetry Test Management Enterprise** instance.  \n2. Navigate to your **user profile** (typically in the top-right corner).  \n3. Select **Settings** or **API Access** from the dropdown menu.\n\n### 2. Generate an API key\n\n1. In the API Access section, click **Generate New API Key.**  \n2. Provide a descriptive **name** for the key (e.g., \"GitLab CI/CD Integration\").  \n3. Set appropriate **permissions**. The key needs write access to upload test results.  \n4. Click **Generate.**  \n5. **Copy the API key immediately** as it will only be displayed once.\n\n**Important security note**: Treat your API key like a password. Never commit it directly to your `.gitlab-ci.yml` file or store it in plain text. We'll use GitLab CI/CD variables to store it securely.\n\n### 3. Note your QMetry instance URL\n\nYou'll also need your QMetry instance URL, which typically follows this format:\n\n```text\nhttps://your-company.qmetry.com\n```\n\nor, for self-hosted instances:\n\n```text\nhttps://qmetry.your-company.com\n```\n\nMake note of this URL because you'll need it in the next section.\n\n## Part 2: Configuring GitLab CI/CD variables\n\nNow that you have your QMetry credentials, let's store them securely in GitLab. Here are the next steps to follow:\n\n### 4. Navigate to CI/CD settings\n\n1. Open your **GitLab project.**  \n2. In the left sidebar, navigate to **Settings > CI/CD.**  \n3. Expand the **Variables** section.  \n4. Click **Add variable.**\n\n### 5. Add the QMetry API key\n\nConfigure the API key variable:\n\n| Field | Value |\n| ----- | ----- |\n| **Key** | `QMETRY_API_KEY` |\n| **Value** | Your QMetry API key from Step 2 |\n| **Type** | Variable |\n| **Flags** | ✅ Mask variable\u003Cbr>✅ Protect variable (recommended) |\n\nClick **Add variable** to save.\n\n### 6. Add the QMetry instance URL\n\nAdd a second variable for your instance URL:\n\n| Field | Value |\n| ----- | ----- |\n| **Key** | `INSTANCE_URL` |\n| **Value** | Your QMetry instance URL (e.g., `https://your-company.qmetry.com`) |\n| **Type** | Variable |\n| **Flags** | (optional: Protect variable) |\n\nClick **Add variable** to save.\n\n**Why use CI/CD variables?**\n\n* **Security**: Masked variables are hidden in job logs.  \n* **Reusability**: You can use the same credentials across multiple pipelines.  \n* **Flexibility**: It is easy to rotate credentials without modifying pipeline code.  \n* **Access control**: Protected variables are only available on protected branches.\n\n## Part 3: Understanding your test result files\n\nBefore integrating the component, ensure your tests generate output files that QMetry can process. Here are the next steps to follow:\n\n### 7. Verify test output format\n\nThe QMetry component supports multiple test result formats. The most common is **JUnit XML**, which most testing frameworks can generate:\n\n**Example JUnit XML output** (`results.xml`):\n\n```xml\n\u003C?xml version=\"1.0\" encoding=\"UTF-8\"?>\n\u003Ctestsuites>\n  \u003Ctestsuite name=\"Flight Control System Tests\" tests=\"15\" failures=\"1\" errors=\"0\" time=\"45.231\">\n    \u003Ctestcase classname=\"FlightControlTests\" name=\"testAltitudeHold\" time=\"2.341\">\n      \u003Csystem-out>Altitude hold engaged at 10,000 feet\u003C/system-out>\n    \u003C/testcase>\n    \u003Ctestcase classname=\"FlightControlTests\" name=\"testAutopilotEngagement\" time=\"3.125\">\n      \u003Csystem-out>Autopilot engaged successfully\u003C/system-out>\n    \u003C/testcase>\n    \u003Ctestcase classname=\"FlightControlTests\" name=\"testEmergencyLanding\" time=\"5.892\">\n      \u003Cfailure message=\"Landing gear failed to deploy\">\n        Expected: Landing gear deployed\n        Actual: Landing gear malfunction detected\n      \u003C/failure>\n    \u003C/testcase>\n    \u003C!-- Additional test cases... -->\n  \u003C/testsuite>\n\u003C/testsuites>\n```\n\nMost testing frameworks generate this format automatically:\n\n* **JUnit** (Java): Native format  \n* **pytest** (Python): Use `--junitxml=results.xml` flag  \n* **Jest** (JavaScript): Use `jest-junit` reporter  \n* **RSpec** (Ruby): Use `rspec_junit_formatter`  \n* **NUnit** (.NET): Use `nunit-console` with XML output  \n* **Go test**: Use `go-junit-report`\n\n### 8. Confirm test artifact configuration\n\nEnsure your existing pipeline saves test results as **artifacts**. This allows the QMetry component to access them:\n\n```yaml\ntest:\n  stage: test\n  script:\n    - npm install\n    - npm test -- --reporter=junit --reporter-options=output=results.xml\n  artifacts:\n    reports:\n      junit: results.xml\n    paths:\n      - results.xml\n    when: always  # Upload even if tests fail\n```\n\n**Key points**:\n\n* `artifacts.reports.junit` makes results visible in GitLab's test report UI.  \n* `artifacts.paths` ensures the file is available to downstream jobs.  \n* `when: always` ensures results upload even if tests fail.\n\n## Part 4: Integrating the QMetry component\n\nNow for the main event – adding the QMetry component to your pipeline. Here are the next steps to follow:\n\n### 9. Basic component integration\n\nAdd the component to your `.gitlab-ci.yml` file. The component should run **after** your tests complete:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\nLet's break down each input parameter:\n\n| Parameter | Description | Example |\n| ----- | ----- | ----- |\n| `stage` | Which CI/CD stage runs the upload job | `test` |\n| `project` | Your QMetry project name or key | `\"Aerospace Flight Control System\"` |\n| `file_name` | Path to your test results file | `\"results.xml\"` |\n| `testing_type` | Format of your test results | `\"JUNIT\"` (also supports: `TESTNG`, `NUNIT`, etc.) |\n| `instance_url` | Your QMetry instance URL | `${INSTANCE_URL}` (from CI/CD variables) |\n| `api_key` | QMetry API key for authentication | `${QMETRY_API_KEY}` (from CI/CD variables) |\n\n### 10. Complete pipeline example\n\nHere's a complete `.gitlab-ci.yml` example showing test execution followed by QMetry upload:\n\n```yaml\nstages:\n  - test\n  - report\n\nvariables:\n  # Your app-specific variables\n  NODE_VERSION: \"18\"\n\n# Run your automated tests\nunit-tests:\n  stage: test\n  image: node:${NODE_VERSION}\n  script:\n    - npm ci\n    - npm run test:unit -- --reporter=junit --reporter-options=output=results.xml\n  artifacts:\n    reports:\n      junit: results.xml\n    paths:\n      - results.xml\n    when: always\n  tags:\n    - docker\n\n# Upload results to QMetry\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test  # Runs in same stage as tests\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n### 11. Run your pipeline\n\nCommit and push your changes:\n\n```shell\ngit add .gitlab-ci.yml\ngit commit -m \"Add QMetry test result integration\"\ngit push origin main\n```\n\nNavigate to your GitLab project's **CI/CD > Pipelines** to watch the execution.\n\n### 12. Verify successful upload\n\nAfter the pipeline completes, you should see:\n\n**In GitLab**:\n\n1. A new job in your pipeline named `qmetry-import` (or similar)  \n2. Job logs showing successful API communication  \n3. Green checkmark indicating successful upload\n\n**Example successful job log**:\n\n```json\n$ curl -X POST https://your-company.qmetry.com/api/v3/test-results/import \\\n  -H \"Authorization: Bearer ${QMETRY_API_KEY}\" \\\n  -H \"Content-Type: application/json\" \\\n  -d @payload.json\n\n{\n  \"status\": \"success\",\n  \"message\": \"Test results uploaded successfully\",\n  \"results_processed\": 15,\n  \"test_cases_created\": 3,\n  \"test_cases_updated\": 12,\n  \"execution_id\": \"EXE-12345\"\n}\n\nJob succeeded ```\n\n**In QMetry**:\n\n1. Navigate to your project dashboard.  \n2. Check the **Test Executions** section.  \n3. You should see a new test execution with results from your GitLab pipeline.  \n4. Click into the execution to see detailed test case results.\n\n\n## Part 5: Advanced configuration options\n\nNow that you have the basic integration working, let's explore advanced configuration for enterprise requirements. Here are the next steps to follow:\n\n### 13. Organizing results with test suites\n\nFor better organization, you can specify which QMetry test suite should receive results:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Sprint 23 Regression Tests\"\n      testsuite_id: \"TS-456\"  # Optional: Use existing test suite ID\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n**When to use test suites**:\n\n* Organizing tests by sprint or release  \n* Separating regression tests from new feature tests  \n* Grouping tests by component or subsystem  \n* Creating test execution hierarchies for reporting\n\n### 14. Configuring automation hierarchy levels\n\nQMetry supports hierarchical test organization. Use the `automation_hierarchy` parameter to specify the organization level:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      automation_hierarchy: \"2\"  # Level 2 hierarchy\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n**Hierarchy levels explained**:\n\n* **Level 1**: Top-level test suites (e.g., \"All Regression Tests\")  \n* **Level 2**: Sub-suites (e.g., \"Flight Control Tests\" under \"Regression Tests\")  \n* **Level 3**: Granular test groups (e.g., \"Altitude Hold Tests\" under \"Flight Control\")\n\n### 15. Multiple test result files\n\nFor complex projects with multiple test jobs, you can invoke the component multiple times:\n\n```yaml\nstages:\n  - test\n\n# Unit tests\nunit-tests:\n  stage: test\n  script:\n    - npm run test:unit\n  artifacts:\n    paths:\n      - unit-results.xml\n    when: always\n\n# Integration tests\nintegration-tests:\n  stage: test\n  script:\n    - npm run test:integration\n  artifacts:\n    paths:\n      - integration-results.xml\n    when: always\n\n# Upload unit test results\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"unit-results.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Unit Tests - Sprint 23\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n\n  # Upload integration test results\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"integration-results.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Integration Tests - Sprint 23\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n### 16. Custom runner tags\n\nFor enterprise environments with dedicated runners, specify runner tags:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      runner_tag: \"production-runners\"  # Use specific runner pool\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n### 17. Custom test suite folders\n\nOrganize test suites into folders for better project structure:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_folder_path: \"/Regression/Sprint-23/Flight-Controls\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\nThis creates a folder hierarchy in QMetry:\n\n```none\nAerospace Flight Control System/\n└── Regression/\n    └── Sprint-23/\n        └── Flight-Controls/\n            └── [Your test execution]\n```\n\n### 18. Advanced field mapping\n\nFor enterprise QMetry instances with custom fields, use the `testcase_fields` and `testsuite_fields` parameters:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      testcase_fields: \"priority=P1,component=FlightControl,certification=DO-178C\"\n      testsuite_fields: \"release=v2.4.0,sprint=23\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\nThis adds custom metadata to test cases and suites for enhanced filtering and reporting.\n\n## Part 6: Real-world use cases\n\nLet's explore how organizations across different industries are using this integration to solve critical quality and compliance challenges.\n\n### Financial services: Enterprise banking platforms\n\nLeading financial institutions are evolving their engineering practices with integrated DevOps platforms. These organizations face unique challenges when managing test automation at scale.\n\n**The challenge for financial services**:\n\n* **Regulatory compliance**: Financial services must maintain detailed audit trails for all testing activities.  \n* **Multiple compliance frameworks**: Firms must adhere to FCA, PSD2, GDPR, and internal risk management policies.  \n* **High-frequency deployments**: Multiple production deployments are required daily across microservices.  \n* **Zero-tolerance for failures**: Banking systems require extremely high reliability.  \n* **Distributed teams**: QA teams need real-time visibility across global engineering teams.\n\n**The solution**: Financial services organizations implementing the QMetry GitLab Component can automate test result uploads across their CI/CD pipelines for:\n\n* Unit tests for hundreds of microservices  \n* API contract tests for inter-service communication  \n* End-to-end transaction flow tests  \n* Security and compliance scanning results  \n* Performance and load testing results\n\n**Example implementation approach**:\n\n```yaml\n# Financial services approach: Separate test uploads by test type\nstages:\n  - test\n  - security\n  - report\n\n# Unit tests for payment processing service\nunit-tests:\n  stage: test\n  script:\n    - mvn clean test\n  artifacts:\n    paths:\n      - target/surefire-reports/TEST-*.xml\n    when: always\n\n# Upload to QMetry with compliance metadata\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: report\n      project: \"Payment Processing Platform\"\n      file_name: \"target/surefire-reports/TEST-*.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Payment Services - Unit Tests\"\n      testsuite_folder_path: \"/Regulatory/FCA-Compliance/Unit-Tests\"\n      testcase_fields: \"compliance=FCA,risk_level=high,service=payments\"\n      automation_hierarchy: \"2\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n**Potential business outcomes for financial services**:\n\n* **Significant reduction** in manual test reporting time  \n* **Complete audit trail coverage** for regulatory reviews  \n* **Real-time visibility** for distributed QA teams  \n* **Faster time-to-production** with automated quality gates  \n* **Enhanced compliance posture** with complete traceability from requirements to test execution\n\n### Aerospace flight control testing\n\nLet's explore how an aerospace company might use this integration for critical flight control system testing.\n\n**Aerospace software development faces unique requirements and challenges:**\n\n* **DO-178C compliance**: Aviation software must follow strict certification standards  \n* **Complete traceability**: Every requirement must link to test cases and execution results  \n* **Audit trails**: Regulators require detailed records of all testing activities  \n* **Safety-critical quality**: Failures can have catastrophic consequences  \n* **Multiple test levels**: Unit, integration, system, and certification tests\n\n**The solution:** By integrating GitLab CI/CD with QMetry, the aerospace engineering team achieves automated test execution and reporting.\n\n\n```yaml\nstages:\n  - build\n  - unit-test\n  - integration-test\n  - system-test\n  - report\n\n# Build flight control firmware\nbuild-firmware:\n  stage: build\n  script:\n    - make clean\n    - make build TARGET=flight-control\n  artifacts:\n    paths:\n      - build/flight-control.bin\n\n# Unit tests (DO-178C Level A)\nunit-tests:\n  stage: unit-test\n  script:\n    - make test-unit OUTPUT=junit\n  artifacts:\n    paths:\n      - test-results/unit-tests.xml\n    when: always\n\n# Hardware-in-the-loop integration tests\nhil-integration-tests:\n  stage: integration-test\n  tags:\n    - hil-test-bench  # Dedicated hardware test environment\n  script:\n    - ./scripts/deploy-to-test-bench.sh\n    - ./scripts/run-hil-tests.sh\n  artifacts:\n    paths:\n      - test-results/hil-tests.xml\n    when: always\n\n# System-level certification tests\ncertification-tests:\n  stage: system-test\n  tags:\n    - certification-environment\n  script:\n    - ./scripts/run-certification-suite.sh\n  artifacts:\n    paths:\n      - test-results/certification-tests.xml\n    when: always\n  only:\n    - main  # Only run on main branch\n\n# Upload unit test results to QMetry\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: report\n      project: \"Flight Control System v2.4\"\n      file_name: \"test-results/unit-tests.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Unit Tests - DO-178C Level A\"\n      testsuite_folder_path: \"/Certification/DO-178C/Unit\"\n      testcase_fields: \"compliance=DO-178C,level=A,safety_critical=true\"\n      automation_hierarchy: \"2\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n\n  # Upload HIL test results\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: report\n      project: \"Flight Control System v2.4\"\n      file_name: \"test-results/hil-tests.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Hardware-in-Loop Integration Tests\"\n      testsuite_folder_path: \"/Certification/DO-178C/Integration\"\n      testcase_fields: \"compliance=DO-178C,level=A,test_type=HIL\"\n      automation_hierarchy: \"2\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n\n  # Upload certification test results\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: report\n      project: \"Flight Control System v2.4\"\n      file_name: \"test-results/certification-tests.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"System Certification Tests\"\n      testsuite_folder_path: \"/Certification/DO-178C/System\"\n      testcase_fields: \"compliance=DO-178C,level=A,certification_ready=true\"\n      automation_hierarchy: \"1\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n### The results\n\n**Before integration**:\n\n* QA engineers manually exported test results from GitLab  \n* Imported results into QMetry through UI uploads  \n* Process took 2-3 hours per test cycle  \n* Human error risk in data entry  \n* Delayed feedback to stakeholders\n\n**After integration**:\n\n* Test results automatically flow from GitLab to QMetry  \n* Complete audit trail from commit → test → result  \n* Zero manual intervention required  \n* Real-time visibility for certification auditors  \n* Compliance reports generated automatically\n\n**Example QMetry dashboard after integration**:\n\n```none\n╔════════════════════════════════════════════════════════════╗\n║  Flight Control System v2.4 - Test Execution Dashboard     ║\n╠════════════════════════════════════════════════════════════╣\n║                                                            ║\n║  📊 Test Execution Summary (Last 7 Days)                   ║\n║  ───────────────────────────────────────────────────────── ║\n║  ✓ Total Tests Executed: 1,247                             ║\n║  ✓ Passed: 1,241 (99.5%)                                   ║\n║  ✗ Failed: 6 (0.5%)                                        ║\n║  ⏸ Skipped: 0                                              ║\n║                                                            ║\n║  📁 Test Suite Organization                                ║\n║  ───────────────────────────────────────────────────────── ║\n║  └─ Certification/                                         ║\n║     └─ DO-178C/                                            ║\n║        ├─ Unit/ (487 tests, 100% pass)                     ║\n║        ├─ Integration/ (623 tests, 99.2% pass)             ║\n║        └─ System/ (137 tests, 100% pass)                   ║\n║                                                            ║\n║  🔗 Traceability                                           ║\n║  ───────────────────────────────────────────────────────── ║\n║  Requirements Covered: 342/342 (100%)                      ║\n║  Test Cases Linked: 1,247/1,247 (100%)                     ║\n║  GitLab Pipeline Executions: 47 (automated)                ║\n║                                                            ║\n║  ⚠️  Action Items                                          ║\n║  ───────────────────────────────────────────────────────── ║\n║  • 6 failed tests require investigation                    ║\n║  • Last execution: 2 minutes ago (Pipeline #1543)          ║\n║  • GitLab Commit: a7f8c23 \"Fix altitude hold logic\"        ║\n║                                                            ║\n╚════════════════════════════════════════════════════════════╝\n```\n\n### Compliance and audit benefits\n\nBoth financial services and aerospace organizations can leverage this integration for compliance:\n\n**For financial services (FCA, PSD2, SOX)**:\n\n1. **Automated traceability**: Link regulatory requirements → test cases → execution results → GitLab commits  \n2. **Audit-ready documentation**: Complete test execution history with timestamps and pipeline references  \n3. **Risk management**: Real-time quality dashboards for risk assessment  \n4. **Regulatory reporting**: Generate compliance reports directly from QMetry test data\n\n**For aerospace certification (DO-178C, DO-254)**:\n\n1. **Automated traceability matrix**: QMetry links requirements → test cases → execution results → GitLab commits  \n2. **Immutable audit trail**: Every test execution is timestamped with pipeline ID, commit SHA, and executor  \n3. **Certification package generation**: QMetry generates compliant documentation pulling data from GitLab pipelines  \n4. **Real-time compliance dashboards**: Auditors can view test coverage and execution history in real-time\n\n## Complete configuration reference\n\nHere's a comprehensive reference of all available component inputs:\n\n| Input Parameter | Required | Default | Description |\n| ----- | ----- | ----- | ----- |\n| `stage` | No | `test` | GitLab CI/CD stage for the upload job |\n| `runner_tag` | No | `\"\"` | Specific runner tag to use (empty = any available runner) |\n| `project` | Yes | - | QMetry project name or key |\n| `file_name` | Yes | - | Path to test results file (relative to project root) |\n| `testing_type` | Yes | - | Test result format: `JUNIT`, `TESTNG`, `NUNIT`, etc. |\n| `skip_warning` | No | `\"1\"` | Skip warnings during import (`\"1\"` = skip, `\"0\"` = show) |\n| `is_matching_required` | No | `\"false\"` | Match existing test cases by name (`\"true\"` or `\"false\"`) |\n| `testsuite_name` | No | `\"\"` | Name for the test suite in QMetry |\n| `testsuite_id` | No | `\"\"` | Existing test suite ID to append results to |\n| `testsuite_folder_path` | No | `\"\"` | Folder path for organizing test suites (e.g., `/Regression/Sprint-23`) |\n| `automation_hierarchy` | No | `\"\"` | Hierarchy level for test organization (`\"1\"`, `\"2\"`, `\"3\"`, etc.) |\n| `testcase_fields` | No | `\"\"` | Custom fields for test cases (comma-separated: `field1=value1,field2=value2`) |\n| `testsuite_fields` | No | `\"\"` | Custom fields for test suites (comma-separated: `field1=value1,field2=value2`) |\n| `instance_url` | Yes | - | QMetry instance URL (store in CI/CD variable) |\n| `api_key` | Yes | - | QMetry API key (store in CI/CD variable, masked) |\n\n## Best practices for production use\n\nAs you scale your integration, follow these best practices:\n\n### Security\n\n* ✅ **Always use CI/CD variables** for sensitive data (API keys, URLs)  \n* ✅ **Mask and protect** API key variables  \n* ✅ **Rotate API keys** periodically (quarterly recommended)  \n* ✅ **Restrict API key permissions** to minimum required (write to test results only)  \n* ✅ **Use protected branches** for production test uploads\n\n### Performance\n\n* ✅ **Keep test result files reasonable size** (\\\u003C 10 MB recommended)  \n* ✅ **Split large test suites** into multiple jobs/files  \n* ✅ **Use parallel test execution** to reduce pipeline duration  \n* ✅ **Cache dependencies** to speed up test execution\n\n### Organization\n\n* ✅ **Use consistent naming conventions** for test suites and folder paths  \n* ✅ **Leverage custom fields** for filtering and reporting  \n* ✅ **Create folder hierarchies** that mirror your test strategy  \n* ✅ **Document your integration** in project README files\n\n### Troubleshooting\n\n* ✅ **Review job logs** for API communication details  \n* ✅ **Verify test result file format** matches `testing_type` parameter  \n* ✅ **Check QMetry project exists** and API key has access  \n* ✅ **Ensure test result files** are available as pipeline artifacts\n\n## Summary and next steps\n\nCongratulations! You've successfully integrated GitLab CI/CD with QMetry Test Management Enterprise. Your setup now provides:\n\n* **Automated test result uploads** – No more manual exports and imports \n\n* **Real-time visibility** – QA teams see results immediately after pipeline execution \n\n* **Complete traceability** – Link GitLab commits, pipelines, and test executions \n\n* **Enhanced compliance** – Maintain audit trails for regulated industries \n\n* **Scalable quality processes** – Support growing test suites without added overhead\n\n### What happens now\n\nEvery time your GitLab pipeline runs:\n\n1. Tests execute and generate result files.  \n2. The QMetry component automatically uploads results to your instance.  \n3. QA teams, stakeholders, and auditors see results in QMetry dashboards.  \n4. AI-powered insights analyze execution patterns and identify improvements.  \n5. Compliance reports generate automatically with full traceability.\n\n### Expand your integration\n\nNow that you have the basic integration working, consider these advanced scenarios:\n\n* **Bi-directional integration**: Use QMetry's API to trigger GitLab pipelines from test management workflows.\n\n* **Multi-project deployments**: Scale the component across your organization's GitLab projects.\n\n* **Custom reporting**: Build dashboards combining GitLab pipeline metrics with QMetry test analytics.\n\n* **Scheduled test execution**: Use GitLab scheduled pipelines to run regression suites nightly.\n\n## Learn more and get help\n\n### Documentation and resources\n\n* **Component documentation**: [GitLab CI/CD Catalog](https://gitlab.com/explore/catalog)  \n* **QMetry documentation**: [QMetry Support Portal](https://qmetrysupport.atlassian.net/wiki/spaces/QPro/overview)  \n* **SmartBear resources**: [SmartBear Academy](https://smartbear.com/resources/)  \n* **GitLab CI/CD documentation**: [GitLab CI/CD Documentation](https://docs.gitlab.com/ee/ci/)\n\n### Support\n\n**For component technical questions**:\n\n* Visit the [component repository](https://gitlab.com/sb9945614/qtm-gitlab-component).  \n* Open an issue on the project.  \n* Check existing issues for common questions.\n\n**For QMetry product questions**:\n\n* Contact SmartBear support at support@smartbear.com.  \n* Visit the [QMetry Community Forum](https://community.smartbear.com/).",{"featured":31,"template":14,"slug":752},"streamline-test-management-with-the-smartbear-qmetry-gitlab-component",{"content":754,"config":763},{"title":755,"description":756,"authors":757,"heroImage":759,"date":749,"body":760,"category":10,"tags":761},"GitLab Duo CLI: Agentic AI for the development lifecycle, now in the terminal","Developers who work outside the IDE and GitLab UI can access GitLab Duo Agent Platform in the terminal with built-in security controls and headless mode support.",[758],"John Coghlan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1775561395/bhe1as7ttjvzltxwgo5m.png","Debugging a broken pipeline at the end of a sprint, or wiring AI into a CI/CD workflow that runs without anyone watching, is exactly where today's AI assistants fall short given their focus on coding – which is only a portion of the software lifecycle. They're built for interactive coding sessions, not automation across different stages of software development. GitLab Duo CLI, now in public beta, is built for both.\n\nGitLab Duo CLI brings agentic AI powered by [Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) to the terminal with full support for automated workflows, alongside an interactive chat mode when you need a human in the loop. This article highlights what Duo CLI does, how its two operating modes work, and the security model behind it.\n\n## How to install GitLab Duo CLI\n\nIf you already have GLab (the GitLab CLI) installed, enter:\n\n```\nglab duo cli\n```\n\nThen follow the prompts.\n\nIf you don't have GLab yet, you can [install it here](https://gitlab.com/gitlab-org/cli/#installation) or [use GitLab Duo CLI as a standalone tool](https://docs.gitlab.com/user/gitlab_duo_cli/#without-the-gitlab-cli).\n\n## Why the terminal, and why now\n\nThe first wave of AI assistants for software development lived in the IDE, and focused solely on coding. That made sense when the job was autocomplete. But as AI agents start *doing things* across every stage of the software lifecycle, e.g. running tests, triggering pipelines, monitoring vulnerability scans, and more, the IDE may no longer be the only abstraction needed to get the job done.\n\nThe best developer tools are ones that work for both humans and machines. CLIs have had decades of design iteration toward that goal. They're composable. You can pipe output, chain commands, and drop them into scripts. They're debuggable: when something goes wrong, you run the same command yourself and see exactly what the agent saw. And they're transparent. No background processes, no initialization dance, no protocol to decode when things break.\n\nTerminal interfaces are better for automation, scripting, and environment portability. IDE interfaces are better for interactive, context-rich development. GitLab Duo CLI is designed for the former, while Duo Agentic Chat in the IDE and UI covers the latter.\n\n## What GitLab Duo CLI can do\n\nWith GitLab Duo CLI, developers can build, modify, refactor, and modernize code — similar to other AI-powered coding assistants built for the terminal. But that’s not where they stop. Any agent and flow defined within GitLab Duo Agent Platform is accessible via Duo CLI, whether it is to automate CI/CD configuration and optimize pipelines, or to perform multi-step development tasks autonomously across the entire software development lifecycle.\n\nGitLab Duo CLI runs in two modes:\n\n* **Interactive mode**, an editor-agnostic terminal chat experience with human-in-the-loop approval before any action is taken. Use it to understand codebase structure, create code, fix errors, or troubleshoot broken pipelines.  \n* **Headless mode**, non-interactive, designed for runners, scripts, and automated workflows. Drop it into CI/CD and let it work without handholding.\n\n## AI with guardrails\n\nAgentic AI that can take actions creates real security exposure. GitLab Duo CLI addresses this at the platform level, not as an afterthought:\n\n* **Human-in-the-loop by default** in interactive mode, so no action is taken without approval.  \n* **Prompt injection detection** is built into the GitLab Duo Agent Platform, not bolted on.  \n* **Composite identity** limits what the agent can access and makes every AI-driven action auditable.\n\nGitLab Duo CLI also supports [custom instruction files](https://docs.gitlab.com/user/duo_agent_platform/customize/), e.g. `chat-rules.md`, `AGENTS.md`, and `SKILL.md`, that define which tasks, resources, context, knowledge, and actions your agents are permitted to take. **This is the principle of least privilege applied to AI: Your agent does exactly what you've authorized, and nothing more.**\n\nWatch GitLab Duo CLI in action:\n\u003Ciframe src=\"https://player.vimeo.com/video/1179964611?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"GitLab Duo CLI Beta Demo V1\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Use GitLab Duo CLI today\n\nYou can experience the benefits of GitLab Duo CLI by [starting a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/). \n\nIf you are already using GitLab in the free tier, you can sign up for GitLab Duo Agent Platform by [following a few simple steps](https://docs.gitlab.com/subscriptions/gitlab_credits/#for-the-free-tier-on-gitlabcom). \n\nAnd if you are an existing subscriber to GitLab Premium or Ultimate, you can take advantage of GitLab Duo CLI by simply [turning on Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/turn_on_off/) and start using the GitLab Credits [that are included](https://docs.gitlab.com/subscriptions/gitlab_credits/#included-credits) with your subscription.",[762,10,27],"AI/ML",{"featured":31,"template":14,"slug":764},"gitlab-duo-cli",{"promotions":766},[767,781,792],{"id":768,"categories":769,"header":771,"text":772,"button":773,"image":778},"ai-modernization",[770],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":774,"config":775},"Get your AI maturity score",{"href":776,"dataGaName":777,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":782,"categories":783,"header":784,"text":772,"button":785,"image":789},"devops-modernization",[10,570],"Are you just managing tools or shipping innovation?",{"text":786,"config":787},"Get your DevOps maturity score",{"href":788,"dataGaName":777,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":793,"categories":794,"header":796,"text":772,"button":797,"image":801},"security-modernization",[795],"security","Are you trading speed for security?",{"text":798,"config":799},"Get your security maturity score",{"href":800,"dataGaName":777,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":802},{"src":803},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":805,"blurb":806,"button":807,"secondaryButton":812},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":808,"config":809},"Get your free trial",{"href":810,"dataGaName":53,"dataGaLocation":811},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":507,"config":813},{"href":57,"dataGaName":58,"dataGaLocation":811},1777309967590]