[{"data":1,"prerenderedAt":804},["ShallowReactive",2],{"/en-us/blog/gitlab-ux-2020-year-in-review":3,"navigation-en-us":40,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Christie Lenneville":702,"blog-related-posts-en-us-gitlab-ux-2020-year-in-review":716,"assessment-promotions-en-us":755,"next-steps-en-us":794},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/gitlab-ux-2020-year-in-review.yml","Gitlab Ux 2020 Year In Review",[7],"christie-lenneville",null,"product",{"slug":11,"featured":12,"template":13},"gitlab-ux-2020-year-in-review",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab UX 2020 Year in Review","2020 was a difficult but productive year. Let's take a look back.",[18],"Christie Lenneville","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664102/Blog/Hero%20Images/gitlab-values-cover.png","2020-11-20","\nA global pandemic and broad social unrest have made this year difficult for everyone. When times are as tough as 2020 has proven to be, it's easy to focus on the negative and forget about the many good things that happened along the way. But our product designers, user researchers, and technical writers spend every day doing great work, and we can't let that slip by unnoticed.\n\nIn this post, I want to be intentional about celebrating our successes during a year when many of us wanted to just curl up under a comfy blanket and wait for the turmoil to pass. So, let's take a moment to reflect on some of the things we can feel really proud to have achieved.\n\n## Usability is now a key consideration in our category maturity model\n\nHistorically, we rated the maturity of our product areas fairly subjectively and based almost entirely on feature availability. This year, that changed when we introduced [Category Maturity Scorecards](https://handbook.gitlab.com/handbook/product/ux/category-maturity/category-maturity-scorecards/) that are based on user research. Now, we start by considering the Job to be Done (JTBD) that our users need to accomplish, and we gather user feedback to rate the entire experience -- not just functionality, but usability, too.\n\nWe've learned some amazing things through this new approach, and those learnings have enabled us to make [valuable recommendations](https://gitlab.com/gitlab-org/gitlab/-/issues?label_name%5B%5D=cm-scorecard-rec) to improve our product experience in areas like Code Review, Logging, and Issue Management. We have several additional scorecard initiatives underway, which means that our focus on creating an exceptional experience will only continue to grow.\n\nSo often, UX departments complain that they have to fight for executives to acknowledge the importance of usability on business outcomes. In this case, refining category maturity started as an idea from [Sid](https://gitlab.com/sytses), our CEO. This is honestly amazing! It's the kind of user-centered focus that UX teams get really excited about.\n\nAs the person who leads UX at GitLab, it was awesome for me to watch our cross-functional team immediately get on board. Because measuring product maturity isn't an industry standard, through our value of [Iteration](https://handbook.gitlab.com/handbook/values/#iteration) it took us some time (and a false start) to determine the right approach. Fortunately, Product leadership was both enthusiastic and patient, UX Researchers were persistent in taking feedback and making methodological refinements, and Product Designers were courageous in trying something they've never done before. Even better: Technical Writing has been involved, too, as we've identified documentation improvements that will refine our product maturity.\n\nThis was truly a team effort, and I appreciate everyone who participated. 🤝\n\n## Our design system evolved from an idea into reality\n\nWhen I joined GitLab in early 2019, our design system, [Pajamas](https://design.gitlab.com/), was a scrappy project that the design team was working hard to get off the ground. We had designed a set of 28 single-source-of-truth components and were working hard to build them into [GitLab UI](https://gitlab.com/gitlab-org/gitlab-ui), our Vue-based component library.\nWe now have a robust design library that's implemented in Figma, and a large collection of SSOT Vue components are available to use in the product, too. Even more exciting: We're just finishing with implementing our 8 most impactful components across the entire product UI (buttons, alerts, dropdowns, modals, tabs, popovers, and tooltips), which will result in better performance and consistency when we're done. (We're so close!)\n\nMost amazing to me was watching product designers and technical writers jump in to do much of this component migration work themselves. This was no small feat, because frontend development is not something that many of us are deeply skilled at. But, apparently we're both tenacious and brave, because we did the work anyway (with lots of help from our Frontend Engineers and the awesome documentation that our UX Foundations team created). In the process, we've gotten to know both our product features (which are complex) and our code base (which is also complex) even better, which makes us more effective in our day-to-day jobs.\n\nSpeaking of our UX Foundations team, this is another related success. At the beginning of 2020, we got the budgetary support to create a team that is dedicated solely to maintaining our design system and tooling. The team may be small, but its impact certainly isn't. They've already made some big improvements to things like:\n\n* **Improving tooling for designers:** The move to Figma allows for greater collaboration, as well as community contributions. Sketch is only available on Mac platforms and there are no real-time collaboration features. Figma allows us to provide a UI Kit that is available across platforms, while being available for community contributors to use for free. It also promotes collaboration through its use of real-time editing capabilities and version history. We were able to streamline developer handoff by simply linking to the design file, reducing the need for additional plugins such as Sketch Measure.\n* **Making our color palette consistent and accessible:** We addressed color contrast for accessibility and normalized the palette across hues, so that we can better systematize variable use throughout the UI.\n* **Improving consistency in our icons:** With the creation of our own [SVG Library](http://gitlab-org.gitlab.io/gitlab-svgs/), we've been working to [deprecate our use of Font Awesome](https://gitlab.com/groups/gitlab-org/-/epics/2331) throughout the year. With the help of the Frontend department, we've closed out 156 out of 168 issues related to this effort.\n* **Moving towards more accessible workflows:** Near the end of the year, we've started focusing more on building accessibility standards into our workflows. We are currently auditing and updating our [voluntary product accessibility template](https://design.gitlab.com/accessibility/vpat), as well as [incorporating accessibility audit guides](https://gitlab.com/gitlab-org/gitlab-services/design.gitlab.com/-/merge_requests/2158) into Pajamas.\n\n## Actionable insights\n\nUser research is so incredibly valuable... when you take action on it. But it can be a challenge for research teams to condense their powerful findings into small but compelling insights and then track those insights to determine whether they actually make it into the product.\n\nIn the second half of this year, our user research team made two big strides in this area. First, we started using [Dovetail](https://dovetailapp.com/) to help us more easily analyze research data to find meangingful insights and share it collaboratively with Product Managers and Product Designers (and anyone else who may be interested). But, they took this a step farther by also beginning to [track actionable insights](https://handbook.gitlab.com/handbook/product/ux/performance-indicators/#actionable-insights) as a performance indicator.\n\nThe considerable effort it took to get both of these programs in place will be worth it as we watch our research efforts result in an even better product.\n\n## Beautifying our docs\n\nComplex products like GitLab require high-quality documentation. Some things you just can't (and shouldn't) communicate through the UI, so users rely on great docs to get their daily jobs done.\n\nOur Technical Writing team (many of whom have been with GitLab less than a year) worked hard to improve our docs site during 2020, including:\n\n- Several UX research projects to discover - and fix! - problems users encounter when using the docs site.\n- A \"Beautification\" effort that focused on an updated visual design. Our 2020 GitLab Contribute event included many rapid improvements to the docs site, and we made many more afterward. (Did you notice?)\n- Ongoing content improvements, including making our docs more consistent, findable, detailed, and easier to read.\n- Adding (a lot of) metadata information to product docs to help connect content contributors with Technical Writers.\n- Coding innovations for automation, such as grammar checking with Vale, a linter, to automatically catch errors before they’re merged.\n\nWe’ve also completed work on a Docs Strategy roadmap to drive even more improvements in the upcoming months.\n\n## And so much more...\n\n* GitLab Design Talks: In this fun video series, watch designers, technical writers, researchers, and product managers talk about [Iteration](https://www.youtube.com/playlist?list=PL05JrBw4t0KpgzLWbRCXf8o7iap-uoe7o) and [Collaboration](https://www.youtube.com/playlist?list=PL05JrBw4t0KrER807JktsL-addVZa4N0-) at GitLab. (Special thanks to host [Nick Post](https://gitlab.com/npost)!)\n* UX Showcase: See [100+ videos](https://www.youtube.com/playlist?list=PL05JrBw4t0Kq89nFXtkVviaIfYQPptwJz) highlighting exciting UX work happening across GitLab. I learn something new everytime I watch one of these.\n* Blog posts: Read about a variety of topics we were thinking about in 2020, including:\n    * [Designing in an all-remote company](https://about.gitlab.com/blog/designing-in-an-all-remote-company/)\n    * [Running an asynchronous sketching workshop for UX](https://about.gitlab.com/blog/async-sketching/)\n    * [Synchronous collaboration as a remote designer at GitLab](https://about.gitlab.com/blog/synchronous-collaboration-as-a-remote-designer-at-gitlab/)\n    * [A tale of two file editors](https://about.gitlab.com/blog/a-tale-of-two-editors/)\n    * [How holistic UX design increased GitLab.com free trial signups](https://about.gitlab.com/blog/how-holistic-ux-design-increased-gitlab-free-trial-signups/)\n    * [Improving iteration and collaboration with user stories](https://about.gitlab.com/blog/how-we-utilize-user-stories-as-a-collaborative-design-tool/)\n    * [Designing incident management from scratch](https://about.gitlab.com/blog/designing-alerts-and-incidents/)\n    * [Why GitLab is the right design collaboration tool for the entire team ](https://about.gitlab.com/blog/why-gitlab-is-the-right-design-collaboration-tool-for-the-whole-team/)\n\nAgain, the GitLab UX team does amazing work every single day, and there is no way to capture all of that effort in a single blog post. As this year wraps up, I hope you personally take time to think about your own successes and the impact they had on our fast-moving company.\n\nI also hope you know that we value every one of you. You are appreciated. 💜\n\n{::options parse_block_html=\"true\" /}\n\n\u003Cdiv class=\"panel panel-gitlab-purple\">\n  \u003Cp class=\"panel-heading\">\u003Cstrong>One more thing...\u003C/strong>\u003C/p>\n\u003Cdiv class=\"panel-body\">\n\n\u003Cp>The final 2020 highlight I wanted to ensure is here was Christie Lenneville's own promotion to be GitLab's first \u003Cstrong>Vice President of User Experience (UX)\u003C/strong>. I knew that as both the author of this article, and as a humble (and great) leader she'd be hesitant to add this herself. But it's not only a recognition of her achievements and her potential. VP-level leadership of UX at GitLab should \u003Ci>also\u003C/i> be a signal of how important UX is to our organization and to our community. And it should indicate that usability is an important differentiator for GitLab, and a critical part of our company's strategy. Congratulations again, Christie!\u003C/p>\n\n— Eric Johnson, Chief Technology Officer\n\n\u003C/div>\n\u003C/div>\n\n{::options parse_block_html=\"false\" /}\n",[23,24,25,26],"UX","design","inside GitLab","research","yml",{},true,"/en-us/blog/gitlab-ux-2020-year-in-review",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":32,"ogSiteName":33,"ogType":34,"canonicalUrls":32},"https://about.gitlab.com/blog/gitlab-ux-2020-year-in-review","https://about.gitlab.com","article","en-us/blog/gitlab-ux-2020-year-in-review",[37,24,38,26],"ux","inside-gitlab","l0rzx_PhvzIb3GXitwFM7VEiMqZAMpO_IwhNXelEhXg",{"data":41},{"logo":42,"freeTrial":47,"sales":52,"login":57,"items":62,"search":370,"minimal":401,"duo":420,"switchNav":429,"pricingDeployment":440},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/","gitlab logo","header",{"text":48,"config":49},"Get free 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Bonner","2026-04-07","In modern software development, test management and continuous integration are two sides of the same coin. 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":29,"template":13,"slug":741},"streamline-test-management-with-the-smartbear-qmetry-gitlab-component",{"content":743,"config":753},{"title":744,"description":745,"authors":746,"heroImage":748,"date":738,"body":749,"category":9,"tags":750},"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.",[747],"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.",[751,9,752],"AI/ML","features",{"featured":29,"template":13,"slug":754},"gitlab-duo-cli",{"promotions":756},[757,771,782],{"id":758,"categories":759,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[760],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[9,570],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":786,"text":762,"button":787,"image":791},"security-modernization",[785],"security","Are you trading speed for security?",{"text":788,"config":789},"Get your security maturity score",{"href":790,"dataGaName":767,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":795,"blurb":796,"button":797,"secondaryButton":802},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":798,"config":799},"Get your free trial",{"href":800,"dataGaName":51,"dataGaLocation":801},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":803},{"href":55,"dataGaName":56,"dataGaLocation":801},1777309969571]