[{"data":1,"prerenderedAt":795},["ShallowReactive",2],{"/en-us/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline":3,"navigation-en-us":34,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Dov Hershkovitch":694,"blog-related-posts-en-us-ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline":708,"assessment-promotions-en-us":747,"next-steps-en-us":785},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":11,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":30,"tagSlugs":31,"__hash__":33},"blogPosts/en-us/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline.yml","Ci Cd Inputs Secure And Preferred Method To Pass Parameters To A Pipeline",[7],"dov-hershkovitch",null,"product",{"featured":11,"template":12,"slug":13},false,"BlogPost","ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"CI/CD inputs: Secure and preferred method to pass parameters to a pipeline","Learn how CI/CD inputs provide type-safe parameter passing with validation, replacing error-prone variables for more reliable pipelines.",[18],"Dov Hershkovitch","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749658912/Blog/Hero%20Images/blog-image-template-1800x945__20_.png","2025-07-07","\nGitLab CI/CD inputs represent the future of pipeline parameter passing. As a purpose-built feature designed specifically for typed parameters with validation, clear contracts, and enhanced security, inputs solve the fundamental challenges that teams have been working around with variables for years.\nWhile CI/CD variables have served as the traditional method for passing parameters to pipelines, they were originally designed for storing configuration settings — not as a sophisticated parameter-passing mechanism for complex workflows. This fundamental mismatch has created reliability issues, security concerns, and maintenance overhead that inputs elegantly eliminate.\nThis article demonstrates why CI/CD inputs should be your preferred approach for pipeline parameters. You'll discover how inputs provide type safety, prevent common pipeline failures, eliminate variable collision issues, and create more maintainable automation. You'll also see practical examples of inputs in action and how they solve real-world challenges, which we hope will encourage you to transition from variable-based workarounds to input-powered reliability.\n## The hidden costs of variable-based parameter passing\nThe problems with using variables for parameter passing are numerous and frustrating. \n**No type validation**\nVariables are strings. There is no type validation, meaning a pipeline expecting a boolean or a number, but accidentally receives a string. This leads to unexpected failures deep into the pipeline execution. In the case of a deployment workflow for example, hours after it was started  a critical production deployment fails because a boolean check in a variable was not passed as expected.\n\n**Runtime mutability**\nVariables can be modified throughout the pipeline runtime, creating unpredictable behavior when multiple jobs attempt to change the same values. For example, deploy_job_a sets `DEPLOY_ENV=staging`, but deploy_job_b changes the `DEPLOY_ENV` value to `production`. \n\n**Security risks**\nSecurity concerns arise because variables intended as simple parameters often receive the same access permissions as sensitive secrets. There's no clear contract defining what parameters a pipeline expects, their types, or their default values. A simple `BUILD_TYPE` parameter, that seems innocuous at first glance, suddenly has access to production secrets simply because variables do not inherently distinguish between parameters and sensitive data.\n\nPerhaps most problematically, error detection happens too late in the process. A misconfigured variable might not cause a failure until minutes or even hours into a pipeline run, wasting valuable CI/CD resources and developer time. Teams have developed elaborate workarounds such as custom validation scripts, extensive documentation, and complex naming conventions just to make variable-based parameter passing somewhat reliable.\nMany users have requested local debugging capabilities to test pipeline configurations before deployment. While this seems like an obvious solution, it quickly breaks down in practice. Enterprise CI/CD workflows integrate with dozens of external systems — cloud providers, artifact repositories, security scanners, deployment targets — that simply can't be replicated locally. Even if they could, the complexity would make local testing environments nearly impossible to maintain. This mismatch forced us to reframe the problem entirely. Instead of asking \"How can we test pipelines locally?\" we started asking \"How can we prevent configuration issues caused by variable-based parameter passing before users run a CI/CD automation workflow?\"\n## Understanding variable precedence\nGitLab's variable system includes multiple [precedence levels](https://docs.gitlab.com/ci/variables/#cicd-variable-precedence) to provide flexibility for different use cases. While this system serves many valid scenarios like allowing administrators to set instance- or group-wide defaults while letting individual projects override them when needed, it can create challenges when building reusable pipeline components.\n\nWhen creating components or templates that will be used across different projects and groups, the variable precedence hierarchy can make behavior less predictable. For example, a template that works perfectly in one project might behave differently in another due to group- or instance-level variable overrides that aren't visible in a pipeline configuration.\n\nWhen including multiple templates, it also can be challenging to track which variables are being set where and how they might interact.\n\nIn addition, components authors need to document not just what variables their template uses, but also potential conflicts with variables that might be defined at higher precedence levels.\n\n### Variable precedence examples\n\n**Main pipeline file (`.gitlab-ci.yml`):**\n\n```yaml\nvariables:\n  ENVIRONMENT: production  # Top-level default for all jobs\n  DATABASE_URL: prod-db.example.com\n\ninclude:\n  - local: 'templates/test-template.yml'\n  - local: 'templates/deploy-template.yml'\n\n```\n\n**Test template (`templates/test-template.yml`):**\n\n```yaml\nrun-tests:\n  variables:\n    ENVIRONMENT: test  # Job-level variable overrides the default\n  script:\n    - echo \"Running tests in $ENVIRONMENT environment\"  \n    - echo \"Database URL is $DATABASE_URL\"  # Still inherits prod-db.example.com!\n    - run-integration-tests --env=$ENVIRONMENT --db=$DATABASE_URL\n    `# Issue: Tests run in \"test\" environment but against production database`\n\n```\n\n**Deploy template (`templates/deploy-template.yml`):**\n\n```yaml\ndeploy-app:\n  script:\n    - echo \"Deploying to $ENVIRONMENT\"  # Uses production (top-level default)\n    - echo \"Database URL is $DATABASE_URL\"  # Uses prod-db.example.com\n    - deploy --target=$ENVIRONMENT --db=$DATABASE_URL\n    # This will deploy to production as intended\n\n```\n**The challenges in this example:**\n\n1. Partial inheritance: The test job gets `ENVIRONMENT=test` but still inherits `DATABASE_URL=prod-db.example.com`.  \n2. Coordination complexity: Template authors must know what top-level variables exist and might conflict.  \n3. Override behavior: Job-level variables with the same name override defaults, but this isn't always obvious.  \n4. Hidden dependencies: Templates become dependent on the main pipeline's variable names.\n\nGitLab recognized these pain points and introduced [CI/CD inputs](https://docs.gitlab.com/ee/ci/inputs/) as a purpose-built solution for passing parameters to pipelines, offering typed parameters with built-in validation that occurs at pipeline creation time rather than during execution.\n\n## CI/CD inputs fundamentals\n\nInputs provide typed parameters for reusable pipeline configuration with built-in validation at pipeline creation time, designed specifically for defining values when the pipeline runs. They create a clear contract between the pipeline consumer and the configuration, explicitly defining what parameters are expected, their types, and constraints.\n\n### Configuration flexibility and scope\n\nOne of the advantages of inputs is their configuration-time flexibility. Inputs are evaluated and interpolated during pipeline creation using the interpolation format `$[[ inputs.input-id ]]`, meaning they can be used anywhere in your pipeline configuration — including job names, rules conditions, images, and any other YAML configuration element. This eliminates the long-standing limitation of variable interpolation in certain contexts.\n\nOne common use case we've seen is that users define their job names like `test-$[[ inputs.environment ]]-deployment`.\n\nWhen using inputs in job names, you can prevent naming conflicts when the same component is included multiple times in a single pipeline. Without this capability, including the same component twice would result in job name collisions, with the second inclusion overwriting the first. Input-based job names ensure each inclusion creates uniquely named jobs.\n\n**Before inputs:**\n\n```yaml\ntest-service:\n  variables:\n    SERVICE_NAME: auth-service\n    ENVIRONMENT: staging\n  script:\n    - run-tests-for $SERVICE_NAME in $ENVIRONMENT\n\n```\n\n**With inputs:**\n\n```yaml\nspec:\n  inputs:\n    environment:\n      type: string\n    service_name:\n      type: string\n\ntest-$[[ inputs.service_name ]]-$[[ inputs.environment ]]:\n  script:\n    - run-tests-for $[[ inputs.service_name ]] in $[[ inputs.environment ]]\n\n```\n\nWhen included multiple times with different inputs, this creates jobs like `test-auth-service-staging`, `test-payment-service-production`, and `test-notification-service-development`. Each job has a unique, meaningful name that clearly indicates its purpose, making pipeline visualization much clearer than having multiple jobs with identical names that would overwrite each other.\n\nNow let's go back to the first example in the top of this blog and use inputs, one immediate benefit is that instead of maintaining multiple templates file we can use one reusable template with different input values:\n\n```yaml\nspec:\n  inputs:\n    environment:\n      type: string\n    database_url:\n      type: string\n    action:\n      type: string\n---\n$[[ inputs.action ]]-$[[ inputs.environment ]]:\n  script:\n    - echo \"Running $[[ inputs.action ]] in $[[ inputs.environment ]] environment\"\n    - echo \"Database URL is $[[ inputs.database_url ]]\"\n    - run-$[[ inputs.action ]] --env=$[[ inputs.environment ]] --db=$[[ inputs.database_url ]]\n\n```\n\nAnd in the main `gitlab-ci.yml` file we can include it twice (or more) with different values, making sure we avoid naming collisions\n\n```yaml\ninclude:\n  - local: 'templates/environment-template.yml'\n    inputs:\n      environment: test\n      database_url: test-db.example.com\n      action: tests\n  - local: 'templates/environment-template.yml'\n    inputs:\n      environment: production\n      database_url: prod-db.example.com\n      action: deploy\n\n```\n\n**The result:** Instead of maintaining separate YAML files for testing and deployment jobs, you now have a single reusable template that handles both use cases safely. This approach scales to any number of environments or job types — reducing maintenance overhead, eliminating code duplication, and ensuring consistency across your entire pipeline configuration. One template to maintain instead of many, with zero risk of variable collision or configuration drift.\n\n### Validation and type safety\n\nAnother key difference between variables and inputs lies in validation capabilities. Inputs support different value types, including strings, numbers, booleans, and arrays, with validation occurring immediately when the pipeline is created. If you define an input as a boolean but pass a string, GitLab will reject the pipeline before any jobs execute, saving time and resources.\n\nHere is an example of the enormous benefit of type validation.\n\n**Without type validation (variables):**\n\n```yaml\nvariables:\n  ENABLE_TESTS: \"true\"  # Always a string\n  MAX_RETRIES: \"3\"      # Always a string\n\ndeploy_job:\n  script:\n    - if [ \"$ENABLE_TESTS\" = true ]; then  # This fails!\n        echo \"Running tests\"\n      fi\n    - retry_count=$((MAX_RETRIES + 1))      # String concatenation: \"31\"\n\n```\n\n**Problem:**  The boolean check fails because “`true`” (string) is not equal to `true`, (boolean).\n\n**With type validation (inputs):**\n\n```yaml\nspec:\n  inputs:\n    enable_tests:\n      type: boolean\n      default: true\n    max_retries:\n      type: number\n      default: 3\n\n      \ndeploy_job:\n  script:\n    - if [ \"$[[ inputs.enable_tests ]]\" = true ]; then  # Works correctly\n        echo \"Running tests\"\n      fi\n    - retry_count=$(($[[ inputs.max_retries ]] + 1))    # Math works: 4\n\n```\n\n**Real-world impact for variable type validation failure**: A developer or a process triggers a GitLab CI/CD pipeline with `ENABLE_TESTS = yes` instead of `true`. Assuming it takes on average 30 minutes before the deployment job starts, then finally when this job kicks off, 30 minutes or longer into the pipeline run, the deployment script tries to evaluate the boolean and fails.  \n\nImagine the impact in terms of time-to-market and, of course. developer time trying to debug why a seemingly basic deploy job failed.\n\nWith type inputs, GitLab CI/CD will immediately throw an error and provide an explicit error message regarding the type mismatch.\n\n### Security and access control\n\nInputs provide enhanced security through controlled parameter passing with explicit contracts that define exactly what values are expected and allowed, creating clear boundaries between parameter passing to the pipeline, In addition. inputs are immutable. Once the pipeline starts, they cannot be modified during execution, providing predictable behavior throughout the pipeline lifecycle and eliminating the security risks that come from runtime variable manipulation.\n\n### Scope and lifecycle\n\nWhen you define variables using the `variables:` keyword at the top level of your `.gitlab-ci.yml` file, these variables become defaults for all jobs in your entire pipeline. When you include templates, you must consider what variables you've defined globally, as they can interact with the template's expected behavior through GitLab's variable precedence order.\n\nInputs are defined in CI configuration files (e.g. components or templates) and assigned values when a pipeline is triggered, allowing you to customize reusable CI configurations. They exist solely for pipeline creation and configuration time, scoped to the CI configuration file where they're defined, and become immutable references once the pipeline begins execution. Since each component maintains its own inputs, there is no risk of inputs interfering with other components or templates in your pipeline, eliminating variable collision and override issues that can occur with variable-based approaches.\n\n## Working with variables and inputs together\n\nWe recognize that teams have extensive investments in their variable-based workflows, and migration to inputs doesn't happen overnight. That's why we've developed capabilities that allow inputs and variables to work seamlessly together, providing a bridge between existing variables and the benefits of inputs while overcoming some key challenges in variable expansion.\n\nLet's look at this real-world example.\n\n**Variable expansion in rules conditions**\n\nA common challenge occurs when using variables that contain other variable references in `rules:if` conditions. GitLab only expands variables one level deep during rule evaluation, which can lead to unexpected behavior:\n\n```yaml\n# This doesn't work as expected\nvariables:\n  TARGET_ENV:\n    value: \"${CI_COMMIT_REF_SLUG}\"\n\ndeploy-job:\n  rules:\n    - if: '$TARGET_ENV == \"production\"'  # Compares \"${CI_COMMIT_REF_SLUG}\" != \"production\"\n      variables:\n        DEPLOY_MODE: \"blue-green\"\n\n```\n\nThe `expand_vars` function solves this by forcing proper variable expansion in inputs:\n```yaml\nspec:\n  inputs:\n    target_environment:\n      description: \"Target deployment environment\"\n      default: \"${CI_COMMIT_REF_SLUG}\"\n---\n\ndeploy-job:\n  rules:\n    - if: '\"$[[ inputs.target_environment | expand_vars ]]\" == \"production\"'\n      variables:\n        DEPLOY_MODE: \"blue-green\"\n        APPROVAL_REQUIRED: \"true\"\n    - when: always\n      variables:\n        DEPLOY_MODE: \"rolling\"\n        APPROVAL_REQUIRED: \"false\"\n  script:\n    - echo \"Target: $[[ inputs.target_environment | expand_vars ]]\"\n    - echo \"Deploy mode: ${DEPLOY_MODE}\"\n\n```\n\n### Why this matters\n\nWithout `expand_vars`, rule conditions evaluate against the literal variable reference (like `\"${CI_COMMIT_REF_SLUG}\"`) rather than the expanded value (like `\"production\"`). This leads to rules that never match when you expect them to, breaking conditional pipeline logic.\n\n**Important notes about expand_vars:**\n\n* Only variables that can be used with the include keyword are supported  \n* Variables must be unmasked (not marked as protected/masked)  \n* Nested variable expansion is not supported  \n* Rule conditions using `expand_vars` must be properly quoted: `'\"$[[ inputs.name | expand_vars ]]\" == \"value\"'`\n\nThis pattern solves the single-level variable expansion limitation, working for any conditional logic that requires comparing fully resolved variable values.\n\n### Function chaining for advanced processing\n\nAlong with `expand_vars`, you can use functions like `truncate` to shorten values for compliance with naming restrictions (such as Kubernetes resource names), creating sophisticated parameter processing pipelines while maintaining input safety and predictability.\n\n```yaml\nspec:  \n  inputs:\n    service_identifier:\n      default: 'service-$CI_PROJECT_NAME-$CI_COMMIT_REF_SLUG'\n---\ncreate-resource:\n  script:\n    - resource_name=$[[ inputs.service_identifier | expand_vars | truncate(0,50) ]]\n\n```\n\nThis integration capability allows you to adopt inputs gradually while leveraging your existing variable infrastructure, making the migration path much smoother.\n\n### From components only to CI pipelines\n\nUp until GitLab 17.11, GitLab users were able to use inputs only in components and templates through the `include:` syntax. This limited their use to reusable CI/CD configurations, but didn't address the broader need for dynamic pipeline customization.\n\n### Pipeline-wide inputs support\n\nStarting with GitLab 17.11, GitLab users can now use inputs to safely modify pipeline behavior across all pipeline execution contexts, replacing the traditional reliance on pipeline variables. This expanded support includes:\n\n* Scheduled pipelines: Define inputs with defaults for automated pipeline runs while allowing manual override when needed.  \n* Downstream pipelines: Pass structured inputs to child and multi-project pipelines with proper validation and type safety.  \n* Manual pipelines: Present users with a clean, validated form interface.\n\nThose enhancements, with more to follow, allow teams to modernize their pipelines while maintaining backward compatibility gradually. Once inputs are fully adopted, users can disable pipeline variables to ensures a more secure and predictable CI/CD environment.\n\n## Summary\n\nThe transition from variables to inputs represents more than just a technical upgrade — it's a shift toward more maintainable, predictable, and secure CI/CD pipelines. While variables continue to serve important purposes for configuration, inputs provide the parameter-passing capabilities that teams have been working around for years.\n\nWe understand that variables are deeply embedded in existing workflows, which is why we've built bridges between the two systems. The `expand_vars` function and other input capabilities allow you to adopt inputs gradually while leveraging your existing variable infrastructure.\n\nBy starting with new components and templates, then gradually migrating high-impact workflows, you'll quickly see the benefits of clearer contracts, earlier error detection, and more reliable automation that scales across your organization. Additionally, moving to inputs creates an excellent foundation for leveraging [GitLab's CI/CD Catalog](https://gitlab.com/explore/catalog), where reusable components with typed interfaces become powerful building blocks for your DevOps workflows but more on that in our next blog post.\n\nYour future self and your teammates will thank you for the clarity and reliability that inputs bring to your CI/CD workflows, while still being able to work with the variable systems you've already invested in.\n\n## What's next \n\nLooking ahead, we're expanding inputs to solve two key challenges: enhancing pipeline triggering with cascading options that [dynamically adjust based on user selections](https://gitlab.com/gitlab-org/gitlab/-/issues/520094), and providing job-level inputs that allow users to [retry individual jobs with different parameter values](https://gitlab.com/groups/gitlab-org/-/epics/17833). We encourage you to follow these discussions, share your feedback, and contribute to shaping these features. You can also provide general feedback on CI/CD inputs through our [feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/407556).\n## Read more\n- [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/) - [CI/CD inputs documentation](https://docs.gitlab.com/ci/inputs/) - [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/) - [GitLab environment variables 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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":27,"template":12,"slug":733},"streamline-test-management-with-the-smartbear-qmetry-gitlab-component",{"content":735,"config":745},{"title":736,"description":737,"authors":738,"heroImage":740,"date":730,"body":741,"category":9,"tags":742},"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.",[739],"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.",[743,9,744],"AI/ML","features",{"featured":27,"template":12,"slug":746},"gitlab-duo-cli",{"promotions":748},[749,763,774],{"id":750,"categories":751,"header":753,"text":754,"button":755,"image":760},"ai-modernization",[752],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":756,"config":757},"Get your AI maturity score",{"href":758,"dataGaName":759,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":761},{"src":762},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":764,"categories":765,"header":766,"text":754,"button":767,"image":771},"devops-modernization",[9,562],"Are you just managing tools or shipping innovation?",{"text":768,"config":769},"Get your DevOps maturity score",{"href":770,"dataGaName":759,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":772},{"src":773},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":775,"categories":776,"header":777,"text":754,"button":778,"image":782},"security-modernization",[24],"Are you trading speed for security?",{"text":779,"config":780},"Get your security maturity score",{"href":781,"dataGaName":759,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":783},{"src":784},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":786,"blurb":787,"button":788,"secondaryButton":793},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":789,"config":790},"Get your free trial",{"href":791,"dataGaName":45,"dataGaLocation":792},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":794},{"href":49,"dataGaName":50,"dataGaLocation":792},1777309999046]