[{"data":1,"prerenderedAt":796},["ShallowReactive",2],{"/en-us/blog/cadence-is-everything-10x-engineering-organizations-for-10x-engineers":3,"navigation-en-us":37,"banner-en-us":437,"footer-en-us":447,"blog-post-authors-en-us-Sid Sijbrandij":687,"blog-related-posts-en-us-cadence-is-everything-10x-engineering-organizations-for-10x-engineers":705,"assessment-promotions-en-us":747,"next-steps-en-us":786},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":36},"blogPosts/en-us/blog/cadence-is-everything-10x-engineering-organizations-for-10x-engineers.yml","Cadence Is Everything 10x Engineering Organizations For 10x Engineers",[7],"sid-sijbrandij",null,"engineering",{"slug":11,"featured":12,"template":13},"cadence-is-everything-10x-engineering-organizations-for-10x-engineers",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Cadence is everything: 10x engineering organizations for 10x engineers","GitLab CEO and co-founder Sid Sijbrandij on the importance of cadence in engineering organizations.",[18],"Sid Sijbrandij","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749671909/Blog/Hero%20Images/Athlinks_running.jpg","2022-11-03","\nI confess: Although I don’t believe in Bigfoot or Nessie and do believe the moon landings happened, I am convinced that despite the current orthodoxies, [10x engineers](https://svdictionary.com/words/10x-engineer) very much exist and are a major positive force for the industry, and potentially your organization. If you can find one, convince her to work for you and keep her happy and productive (but I repeat myself).\n\nAlas, finding one is not easy, and no, job adverts stating “We only hire the best” don’t help. However, what you can do is structure your development organization in a way to make such a person productive. \n\nFortunately, making a 10x developer productive is pretty much the same, as you need to make your development organization productive for everyone, just dialed up to 11, particularly because an inefficient organization will affect a more efficient developer much more dramatically.\n\nUnfortunately, this state appears to be neither natural nor stable.\n\n[Effective organizations are unnatural](https://twitter.com/paulg/status/1556341452740775936?s=21&t=67hekF4Sus5tPryLdZmCHA). The natural state of organizations is bureaucracy and turf wars, and once deprived of effective leadership they revert to their natural state with shocking speed. Similar to organizations in general, development organizations naturally tend toward inefficiency.\n\nMore specifically, development organizations tend toward ever-lengthening cycle times just as much as organizations in general tend toward bureaucracy. In both cases, this is always for good reasons. This is really important: If this tendency toward lengthening cycle times were just stupidity or laziness, it would be significantly easier to counter. Anthropologist and historian [Joseph Tainter makes a similar point](https://www.youtube.com/watch?v=JsT9V3WQiNA) about civilizations, whose ever-increasing complexity leads to their collapse. Here as well, the complexity is not introduced willy-nilly, but as a necessary response to problems the civilization faces. \n\n## The sky’s the limit\n\nSoftware tends to be fairly abstract, but the principles of short cycle times are just applicable in more down-to-earth disciplines, or should I say down-to-air? First, one of my favorites, the story of how Paul MacCready created the Gossamer Condor to win the first Kremer Prize for human-powered flight. More recently, Elon Musk’s SpaceX has been out-iterating NASA and the legacy spaceflight companies with results that would have seemed miraculous a couple of decades ago. Both examples show that while other factors are obviously more important, cadence actually dominates them in short order.\n\nMacGready had come into a bit of debt due to securing a friend’s business loan, and set his eyes on the first Kremer prize for human-powered flight. This had gone unclaimed for 17 years, but not for lack of trying: There had been over 50 official attempts and all failed. It was a Very Hard Problem we couldn’t solve so it obviously required the most aerodynamically efficient and sophisticated designs possible. So that’s what people did, and when their sophisticated plane inevitably crashed — after all they were working on the edge of the possible — it took them a year or more to rebuild it.\n\nMacGready approached this from the opposite angle: He would concentrate on a plane that didn’t have to be so efficient and sophisticated, but instead would fly low and slow, be light and very repairable, aiming for 12 crashes a day. The Gossamer Condor was built out of some lightweight aluminum struts and mylar foil and could usually be repaired with Scotch tape. It was a weird contraption that didn’t look like it could fly.\n\n![The Condor](https://about.gitlab.com/images/blogimages/10x.png)\n\nWithin a few months, the team had accumulated more flights, and more crashes, than the rest of the competition combined. With all that experience, they then also understood the actual problems better than anyone else, for example, how to steer, and soon won the prize, which involved flying a mile in a circle eight. \n\nThis wasn’t a [one-off fluke](https://www.youtube.com/watch?v=FvmTSpJU-Xc&t=3348s) either: The team went on to win the next Kremer prize as well, crossing the English Channel, then pioneered solar flight and broke the SR-71’s altitude record. The company that came out of the effort nowadays makes drones, including the successful Switchblade drones for the U.S. military that have recently been sent to help in the Ukraine conflict.\n\n## The sky’s not the limit\n\nMore recently, SpaceX has been demonstrating the efficacy of iterative development, first with the Falcon 9 rocket and now with the Starship program. While the latter hasn’t flown to space yet, and so may still fail completely, both the aim and the achievements so far have been breathtaking, particularly compared to NASA’s Space Launch System (SLS), which was started around the same time and is designed to have similar capabilities, lifting around 100 tons to low earth orbit.\n\nThe NASA SLS is a cost-reduced version of the Constellation program, which was canceled early after quickly outgrowing its projected $150 billion dollar budget. The reduced development cost of the SLS (so far $23 billion in 10 years) has been achieved by reusing not just designs, but also actual parts from the Space Shuttle program. Not just the solid rocket boosters, but some of the main engines are the actual parts that flew on shuttles and had been mothballed by NASA. Despite this part reuse, launches of the fully expendable rocket are predicted to cost somewhat upward of $1 billion per pop. As of Oct. 20, there have been no flights of any of the hardware (except on space shuttles), and the first test launch scheduled for Nov. 26th will fly the full stack as designed.\n\nIn comparison, the Starship program is estimated to have cost $3 billion so far, with estimates of total development costs varying between $5 billion and $10 billion. This is for a completely new rocket, pretty much unlike any that have come before, designed for full reusability and same-day turnaround after refueling, completely new methane-burning engines, assembly-line production using relatively inexpensive materials and a projected cost target of $10 million per launch. If they work as advertised, just a few Starships could turn the entire launch capacity of planet Earth thus far into a footnote, a rounding error, and they plan to build a thousand of them. That’s why they’re building a factory for making them.\n\nIt’s anyone’s guess whether all this launch capacity, at costs two or more magnitudes lower than currently possible, is really for making humanity multiplanetary by establishing a Mars colony or “just” for making space-based production and asteroid mining feasible.\n\nWhen asked, [Elon Musk put it quite simply](https://www.youtube.com/watch?v=E7MQb9Y4FAE&t=333s):\n\n_“Any given technology development is how many iterations do you have and what’s your time and progress between iterations.”_\n\nThe more quickly you can iterate, the more iterations you have available. But doesn’t iterating more quickly make the progress between iterations correspondingly less, canceling the effect? Surprisingly, that turns out not to be the case. Elon Musk again:\n\n_“So if you have a high production rate, you can have a lot of iterations. You can try lots of different things, and it’s OK if you blow up an engine because you’ve got a number of engines coming after that. If you have a small number of engines then you have to be much more conservative, because you can’t risk blowing them up.”_\n\nThe higher iteration rate allows you to take more risks, which in turn allows you to push the boundaries more and thus gather more relevant feedback in each iteration, at the same time that the reduced time frame reduces what you can do. So there will be more failures. For example, engines blowing up or planes crashing. But as long as the failures provide the information they were supposed to provide, and the individual failure modes aren’t fatal, they aren’t actually failures. You obviously don’t want to be cavalier about this, but accepting that risk allows you to push much farther per iteration. Musk also mentioned that as one of the main problems of the Space Shuttle program: They couldn’t afford to have one blow up because even the first flight was manned.\n\n“A high production rate solves many ills,” he says.\n\nIn software, the production rate is the iteration rate. If you have lots of iterations, it’s OK if one of them was a potentially high-value experiment that didn’t pan out. If you have one iteration per year, you are less likely to want to take that risk, and your reluctance will be justified. The willingness and ability to take risks is captured in the Extreme Programming (XP) [value of ‘courage.”](http://xp.c2.com/ExtremeValues.html)\n\n## Compound interest and experience\n\nThe reason this works out is mathematical. If you iterate and actually use the feedback the iteration gives you to improve, you will improve a little bit each time because you will have learned something. For simplicity’s sake, let’s assume an improvement of 5% per iteration. This is like compound interest, and while it starts slow, once it ramps up, it gives outsize returns, like any exponential.\n\nImprove 2% per iteration, and after three iterations, you will have improved by 6%, which is essentially the same as a linear improvement. After 200 iterations, however, and whereas the linear approach will have improved by a respectable factor of 4, the iterative approach will have improved by more than 50x.\n\nApart from the purely mathematical, there is also the human factor: When we do things over and over again, we really start to figure out how it works. We develop an intuition.\n\n## What the science says\n\nThe simplistic mathematical function is obviously not an accurate model of the real world, but the science actually has concluded that higher iteration rates are the one most important factor for the output of software development teams, at least according to the researchers. These findings have been published in the book “[Accelerate](https://itrevolution.com/product/accelerate/)\" by Nicole Forsgren, Jez Humble and Gene Kim. The authors have since moved to Google as the DevOps Research and Assessment (DORA) team and make their [findings available here](https://cloud.google.com/blog/products/devops-sre/announcing-dora-2021-accelerate-state-of-devops-report). \n\nIn short, they find that performance of software teams correlates strongly with cycle times, with the lowest- performing teams having cycle times measured in months, medium performers in weeks, good performers in days and excellent performers in hours. There is also good evidence for the causality going for cycle times to performance and not the other way around.\n\nBut there’s a deeper connection, because the method of iterating on real-world feedback is really just the scientific method, no more, no less. It is somewhat surprising that in the field of software, we still often consider the scientific method as unruly and dangerous “cowboy coding,” and instead advocate for what is really little different from pre-science scholasticism as the proper approach to creating software.\n\nTo help us also be more scientific and data driven, the DORA team created metrics, called the [DORA metrics](https://cloud.google.com/blog/products/devops-sre/using-the-four-keys-to-measure-your-devops-performance). These are the following:\n\n- Deployment frequency — How often an organization successfully releases to production\n- Lead time for changes — The amount of time it takes a commit to get into production\n- Change failure rate — The percentage of deployments causing a failure in production\n- Time to restore service — How long it takes an organization to recover from a failure in production\n\n## The dangers of dead reckoning\n\nIn reality, it is much more dangerous to stay away from actual code and real feedback from users for any length of time. For example, ships before GPS used essentially two methods for navigation: dead reckoning and external fixes. With dead reckoning, you took a known position, added the course speed and known currents over time to come up with a new position. However, despite the best equipment and methods, this method always introduces some error because the external factors cannot be known with certainty. And what’s worse, just like improvements accumulate and build on each other over time, so do these errors, making the position ever more uncertain over time.\n\nWhen you are in the middle of the ocean, that might not be a huge problem, but close to shore it can be deadly, which is why the amphibious ships of the Royal Navy were required to use position fixing in intervals of a few minutes. With position fixing, you use the actual external environment, landmarks that you can triangulate to determine your position (and of course GPS is just a version of this, except using satellites for the fix instead of landmarks). This means you aren’t guessing where you are, you know where you are, and every new measurement clears the slate of any errors; there is no accumulation.\n\nSlides don’t crash, and Jira is patient. You can have 100 tasks that are marked as 99% completed in your tracker of choice and still never ship anything to customers.\n\nReality is that which, when you stop believing in it, doesn’t go away, said science fiction writer Phillip K. Dick, in [How to Build a Universe that Doesn’t Fall Apart Two Days Later](https://deoxy.org/pkd_how2build.htm).\n\nIn Part 2, The Process Equation, we will look at overcoming the forces that tend to push software engineering organizations toward higher cycle times and lower 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statement",{"items":677},[678,681,684],{"text":679,"config":680},"Terms",{"href":507,"dataGaName":508,"dataGaLocation":455},{"text":682,"config":683},"Cookies",{"dataGaName":517,"dataGaLocation":455,"id":518,"isOneTrustButton":27},{"text":685,"config":686},"Privacy",{"href":512,"dataGaName":513,"dataGaLocation":455},[688],{"id":689,"title":18,"body":8,"config":690,"content":692,"description":8,"extension":25,"meta":700,"navigation":27,"path":701,"seo":702,"stem":703,"__hash__":704},"blogAuthors/en-us/blog/authors/sid-sijbrandij.yml",{"template":691},"BlogAuthor",{"role":693,"name":18,"bio":694,"config":695},"Co-founder, Chief Executive Officer and Board Chair of GitLab Inc.","Sid Sijbrandij (pronounced see-brandy) is the Co-founder, Chief Executive Officer and Board Chair of GitLab Inc., the most comprehensive AI-powered DevSecOps platform. GitLab's single application helps organizations deliver software faster and more efficiently while strengthening their security and compliance.\n\nSid's career path has been anything but traditional. He spent four years building recreational submarines for U-Boat Worx and while at Ministerie van Justitie en Veiligheid he worked on the Legis project, which developed several innovative web applications to aid lawmaking. He first saw Ruby code in 2007 and loved it so much that he taught himself how to program. In 2012, as a Ruby programmer, he encountered GitLab and discovered his passion for open source. Soon after, Sid commercialized GitLab, and by 2015 he led the company through Y Combinator's Winter 2015 batch. Under his leadership, the company has grown with an estimated 30 million+ registered users from startups to global enterprises.\n\nSid studied at the University of Twente in the Netherlands where he received an M.S. in Management Science. Sid was named one of the greatest minds of the pandemic by Forbes for spreading the gospel of remote work.",{"headshot":696,"twitter":697,"linkedin":698,"ctfId":699},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665383/Blog/Author%20Headshots/sytses-headshot.png","https://twitter.com/sytses","https://www.linkedin.com/in/sijbrandij","sytses",{},"/en-us/blog/authors/sid-sijbrandij",{},"en-us/blog/authors/sid-sijbrandij","ZdVvFbtL6NKLtKZEjFCVOecdpvuPzX3wmEZBrC6pRWg",[706,719,731],{"content":707,"config":717},{"title":708,"description":709,"authors":710,"heroImage":712,"date":713,"category":9,"tags":714,"body":716},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[711],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[259,609,715],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":718,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":720,"config":729},{"title":721,"description":722,"authors":723,"heroImage":724,"date":725,"category":9,"tags":726,"body":728},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[711],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[609,259,727],"product","Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1. **Deliver a modern, integrated DevSecOps curriculum.**\n\n2. **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3. **Empower students to make AI-assisted open source contributions.**\n\n4. **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":730,"featured":27,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"content":732,"config":745},{"category":9,"tags":733,"body":736,"date":737,"updatedDate":738,"heroImage":739,"authors":740,"title":743,"description":744},[734,735,106],"tutorial","git","\nEnterprise teams are increasingly migrating from Azure DevOps to GitLab to gain strategic advantages and accelerate secure software delivery. \n\n\n- GitLab comes with integrated controls, policies, and [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that allow organizations to implement software delivery standards at scale. This is especially important for regulated industries.\n\n- [Security testing](https://docs.gitlab.com/user/application_security/) is embedded in the pipeline and results show in the developer workflow, including static application security testing (SAST), source code analysis (SCA), dynamic application security testing (DAST), infrastructure-as-code scanning (IaC), container scanning, and API scanning.\n\n- [AI capabilities](https://about.gitlab.com/gitlab-duo-agent-platform/) across the full software delivery lifecycle include advanced agent orchestration and customizable flows to support how your organizational teams work.\n\n\nGitLab's open-source, open-core approach, flexible deployment options such as single-tenant dedicated and self-managed, and truly unified platform eliminate integration complexity and security gaps. \n\n\nFor teams facing mounting pressure to accelerate delivery while strengthening security posture and maintaining regulatory compliance, GitLab represents not just a migration but a platform evolution.\n\n\nMigrating from Azure DevOps to GitLab can seem like a daunting task, but with the right approach and tools, it can be a smooth and efficient process. This guide will walk you through the steps needed to successfully migrate your projects, repositories, and pipelines from Azure DevOps to GitLab.\n\n\n## Overview\n\nGitLab provides both [Congregate](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/) (maintained by [GitLab Professional Services](https://about.gitlab.com/professional-services/) organization) and [a built-in Git repository import](https://docs.gitlab.com/user/project/import/repo_by_url/) for migrating projects from Azure DevOps (ADO). These options support repository-by-repository or bulk migration and preserve git commit history, branches, and tags. With Congregate and professional services tools, we support additional assets such as wikis, work items, CI/CD variables, container images, packages, pipelines, and more (see this [feature matrix](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/blob/master/customer/ado-migration-features-matrix.md)). Use this guide to plan and execute your migration and complete post-migration follow-up tasks.\n\n\nEnterprises migrating from ADO to GitLab commonly follow a multi-phase approach:\n\n\n- Migrate repositories from ADO to GitLab using Congregate or GitLab's built-in repository migration.\n\n- Migrate pipelines from Azure Pipelines to GitLab CI/CD.\n\n- Migrate remaining assets such as boards, work items, and artifacts to GitLab Issues, Epics, and the Package and Container Registries.\n\n\nHigh-level migration phases:\n\n\n```mermaid\ngraph LR\n subgraph Prerequisites\n direction TB\n A[\"Set up identity provider (IdP) and\u003Cbr/>provision users\"]\n A --> B[\"Set up runners and\u003Cbr/>third-party integrations\"]\n B --> I[\"Users enablement and\u003Cbr/>change management\"]\n end\n \n subgraph MigrationPhase[\"Migration phase\"]\n direction TB\n C[\"Migrate source code\"]\n C --> D[\"Preserve contributions and\u003Cbr/> format history\"]\n D --> E[\"Migrate work items and\u003Cbr/>map to \u003Ca href=\"https://docs.gitlab.com/topics/plan_and_track/\">GitLab Plan \u003Cbr/>and track work\"]\n end\n \n subgraph PostMigration[\"Post-migration steps\"]\n direction TB\n F[\"Create or translate \u003Cbr/>ADO pipelines to GitLab CI\"]\n F --> G[\"Migrate other assets\u003Cbr/>packages and container images\"]\n G --> H[\"Introduce \u003Ca href=\"https://docs.gitlab.com/user/application_security/secure_your_application/\">security\u003C/a> and\u003Cbr/>SDLC improvements\"]\n end\n \n Prerequisites --> MigrationPhase\n MigrationPhase --> PostMigration\n\n style A fill:#FC6D26\n style B fill:#FC6D26\n style I fill:#FC6D26\n style C fill:#8C929D\n style D fill:#8C929D\n style E fill:#8C929D\n style F fill:#FFA500\n style G fill:#FFA500\n style H fill:#FFA500\n```\n\n\n## Planning your migration\n\n\n**To plan your migration, ask these questions:**\n\n\n- How soon do we need to complete the migration?\n\n- Do we understand what will be migrated?\n\n- Who will run the migration?\n\n- What organizational structure do we want in GitLab?\n\n- Are there any constraints, limitations, or pitfalls that need to be taken into account?\n\n\nDetermine your timeline, as it will largely dictate your migration approach. Identify champions or groups familiar with both ADO and GitLab platforms (such as early adopters) to help drive adoption and provide guidance.\n\n\n**Inventory what you need to migrate:**\n\n\n- The number of repositories, pull requests, and contributors\n\n- The number and complexity of work items and pipelines\n\n- Repository sizes and dependency relationships\n\n- Critical integrations and runner requirements (agent pools with specific capabilities)\n\n\nUse GitLab Professional Services's [Evaluate](https://gitlab.com/gitlab-org/professional-services-automation/tools/utilities/evaluate#beta-azure-devops) tool to produce a complete inventory of your entire Azure DevOps organization, including repositories, PR counts, contributor lists, number of pipelines, work items, CI/CD variables and more. If you're working with the GitLab Professional Services team, share this report with your engagement manager or technical architect to help plan the migration.\n\n\nMigration timing is primarily driven by pull request count, repository size, and amount of contributions (e.g. comments in PR, work items, etc). For example, 1,000 small repositories with few PRs and limited contributors can migrate much faster than a smaller set of repositories containing tens of thousands of PRs and thousands of contributors. Use your inventory data to estimate effort and plan test runs before proceeding with production migrations.\n\n\nCompare inventory against your desired timeline and decide whether to migrate all repositories at once or in batches. If teams cannot migrate simultaneously, batch and stagger migrations to align with team schedules. For example, in Professional Services engagements, we organize migrations into waves of 200-300 projects to manage complexity and respect API rate limits, both in [GitLab](https://docs.gitlab.com/security/rate_limits/) and [ADO](https://learn.microsoft.com/en-us/azure/devops/integrate/concepts/rate-limits?view=azure-devops).\n\n\nGitLab's built-in [repository importer](https://docs.gitlab.com/user/project/import/repo_by_url/) migrates Git repositories (commits, branches, and tags) one-by-one. Congregate is designed to preserve pull requests (known in GitLab as merge requests), comments, and related metadata where possible; the simple built-in repository import focuses only on the Git data (history, branches, and tags).\n\n\n**Items that typically require separate migration or manual recreation:**\n\n\n- Azure Pipelines - create equivalent GitLab CI/CD pipelines (consult with [CI/CD YAML](https://docs.gitlab.com/ci/yaml/) and/or with [CI/CD components](https://docs.gitlab.com/ci/components/)). Alternatively, consider using AI-based pipeline conversion available in Congregate.\n\n- Work items and boards - map to GitLab Issues, Epics, and Issue Boards.\n\n- Artifacts, container images (ACR) - migrate to GitLab Package Registry or Container Registry.\n\n- Service hooks and external integrations - recreate in GitLab.\n\n- [Permissions models](https://docs.gitlab.com/user/permissions/) differ between ADO and GitLab; review and plan permissions mapping rather than assuming exact preservation.\n\n\nReview what each tool (Congregate vs. built-in import) will migrate and choose the one that fits your needs. Make a list of any data or integrations that must be migrated or recreated manually.\n\n\n**Who will run the migration?**\n\n\nMigrations are typically run by a GitLab group owner or instance administrator, or by a designated migrator who has been granted the necessary permissions on the destination group/project. Congregate and the GitLab import APIs require valid authentication tokens for both Azure DevOps and GitLab.\n\n\n- Decide whether a group owner/admin will perform the migrations or whether you will grant a specific team/person delegated access.\n\n- Ensure the migrator has correctly configured personal access tokens (Azure DevOps and GitLab) with the scopes required by your chosen migration tool (for example, api/read_repository scopes and any tool-specific requirements). \n\n- Test tokens and permissions with a small pilot migration.\n\n**Note:** Congregate leverages file-based import functionality for ADO migrations and requires instance administrator permissions to run ([see our documentation](https://docs.gitlab.com/user/project/settings/import_export/#migrate-projects-by-uploading-an-export-file)). If you are migrating to GitLab.com, consider engaging Professional Services. For more information, see the [Professional Services Full Catalog](https://about.gitlab.com/professional-services/catalog/). Non-admin account cannot preserve contribution attribution!\n\n\n**What organizational structure do we want in GitLab?**\n\nWhile it's possible to map ADO structure directly to GitLab structure, it's recommended to rationalize and simplify the structure during migration. Consider how teams will work in GitLab and design the structure to facilitate collaboration and access management. Here is a way to think about mapping ADO structure to GitLab structure:\n\n\n```mermaid\ngraph TD\n subgraph GitLab\n direction TB\n A[\"Top-level Group\"]\n B[\"Subgroup (optional)\"]\n C[\"Projects\"]\n A --> B\n A --> C\n B --> C\n end\n\n subgraph AzureDevOps[\"Azure DevOps\"]\n direction TB\n F[\"Organizations\"]\n G[\"Projects\"]\n H[\"Repositories\"]\n F --> G\n G --> H\n end\n\n style A fill:#FC6D26\n style B fill:#FC6D26\n style C fill:#FC6D26\n style F fill:#8C929D\n style G fill:#8C929D\n style H fill:#8C929D\n```\n\nRecommended approach:\n\n\n- Map each ADO organization to a GitLab group (or a small set of groups), not to many small groups. Avoid creating a GitLab group for every ADO team project. Use migration as an opportunity to rationalize your GitLab structure.\n\n- Use subgroups and project-level permissions to group related repositories.\n\n- Manage access to sets of projects by using GitLab groups and group membership (groups and subgroups) rather than one group per team project.\n\n- Review GitLab [permissions](https://docs.gitlab.com/ee/user/permissions.html) and consider [SAML Group Links](https://docs.gitlab.com/user/group/saml_sso/group_sync/) to implement an enterprise RBAC model for your GitLab instance (or a GitLab.com namespace).\n\n\n**ADO Boards and work items: State of migration**\n\n\nIt's important to understand how work items migrate from ADO into GitLab Plan (issues, epics, and boards).\n\n\n- ADO Boards and work items map to GitLab Issues, Epics, and Issue Boards. Plan how your workflows and board configurations will translate.\n\n- ADO Epics and Features become GitLab Epics.\n\n- Other work item types (e.g., user stories, tasks, bugs) become project-scoped issues.\n\n- Most standard fields are preserved; selected custom fields can be migrated when supported.\n\n- Parent-child relationships are retained so Epics reference all related issues.\n\n- Links to pull requests are converted to merge request links to maintain development traceability.\n\n\nExample: Migration of an individual work item to a GitLab Issue, including field accuracy and relationships:\n\n\n![Example: Migration of an individual work item to a GitLab Issue](https://res.cloudinary.com/about-gitlab-com/image/upload/v1764769188/ztesjnxxfbwmfmtckyga.png)\n\n\nBatching guidance:\n\n\n- If you need to run migrations in batches, use your new group/subgroup structure to define batches (for example, by ADO organization or by product area).\n\n- Use inventory reports to drive batch selection and test each batch with a pilot migration before scaling.\n\n\n**Pipelines migration**\n\n\nCongregate [recently introduced](https://gitlab.com/gitlab-org/professional-services-automation/tools/migration/congregate/-/merge_requests/1298) AI-powered conversion for multi-stage YAML pipelines from Azure DevOps to GitLab CI/CD. This automated conversion works best for simple, single-file pipelines and is designed to provide a working starting point rather than a production-ready `.gitlab-ci.yml` file. The tool generates a functionally equivalent GitLab pipeline that you can then refine and optimize for your specific needs.\n\n\n- Converts Azure Pipelines YAML to `.gitlab-ci.yml` format automatically.\n\n- Best suited for straightforward, single-file pipeline configurations.\n\n- Provides a boilerplate to accelerate migration, not a final production artifact.\n\n- Requires review and adjustment for complex scenarios, custom tasks, or enterprise requirements.\n\n- Does not support Azure DevOps classic release pipelines — [convert these to multi-stage YAML](https://learn.microsoft.com/en-us/azure/devops/pipelines/release/from-classic-pipelines?view=azure-devops) first.\n\n\nRepository owners should review the [GitLab CI/CD documentation](https://docs.gitlab.com/ci/) to further optimize and enhance their pipelines after the initial conversion.\n\n\nExample of converted pipelines:\n\n\n```yml \n\n# azure-pipelines.yml\n\ntrigger:\n - main\n\nvariables:\n imageName: myapp\n\nstages:\n - stage: Build\n jobs:\n - job: Build\n pool:\n vmImage: 'ubuntu-latest'\n steps:\n - checkout: self\n\n - task: Docker@2\n displayName: Build Docker image\n inputs:\n command: build\n repository: $(imageName)\n Dockerfile: '**/Dockerfile'\n tags: |\n $(Build.BuildId)\n\n - stage: Test\n jobs:\n - job: Test\n pool:\n vmImage: 'ubuntu-latest'\n steps:\n - checkout: self\n\n # Example: run tests inside the container\n - script: |\n docker run --rm $(imageName):$(Build.BuildId) npm test\n displayName: Run tests\n\n - stage: Push\n jobs:\n - job: Push\n pool:\n vmImage: 'ubuntu-latest'\n steps:\n - checkout: self\n\n - task: Docker@2\n displayName: Login to ACR\n inputs:\n command: login\n containerRegistry: '\u003Cyour-acr-service-connection>'\n\n - task: Docker@2\n displayName: Push image to ACR\n inputs:\n command: push\n repository: $(imageName)\n tags: |\n $(Build.BuildId)\n\n```\n\n```yaml\n\n# .gitlab-ci.yml\n\nvariables:\n imageName: myapp\n\nstages:\n - build\n - test\n - push\n\nbuild:\n stage: build\n image: docker:latest\n services:\n - docker:dind\n script:\n - docker build -t $imageName:$CI_PIPELINE_ID -f $(find . -name Dockerfile) .\n only:\n - main\n\ntest:\n stage: test\n image: docker:latest\n services:\n - docker:dind\n script:\n - docker run --rm $imageName:$CI_PIPELINE_ID npm test\n only:\n - main\n\npush:\n stage: push\n image: docker:latest\n services:\n - docker:dind\n before_script:\n - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n script:\n - docker tag $imageName:$CI_PIPELINE_ID $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n - docker push $CI_REGISTRY/$CI_PROJECT_PATH/$imageName:$CI_PIPELINE_ID\n only:\n - main\n\n```\n\n**Final checklist:**\n\n\n- Decide timeline and batch strategy.\n\n- Produce a full inventory of repositories, PRs, and contributors.\n\n- Choose Congregate or the built-in import based on scope (PRs and metadata vs. Git data only).\n\n- Decide who will run migrations and ensure tokens/permissions are configured.\n\n- Identify assets that must be migrated separately (pipelines, work items, artifacts, and hooks) and plan those efforts.\n\n- Run pilot migrations, validate results, then scale according to your plan.\n\n\n## Running your migrations\n\n\nAfter planning, execute migrations in stages, starting with trial runs. Trial migrations help surface org-specific issues early and let you measure duration, validate outcomes, and fine-tune your approach before production.\n\n\nWhat trial migrations validate:\n\n\n- Whether a given repository and related assets migrate successfully (history, branches, tags; plus MRs/comments if using Congregate)\n\n- Whether the destination is usable immediately (permissions, runners, CI/CD variables, integrations)\n\n- How long each batch takes, to set schedules and stakeholder expectations\n\n\nDowntime guidance:\n\n\n- GitLab's built-in Git import and Congregate do not inherently require downtime.\n\n- For production waves, freeze changes in ADO (branch protections or read-only) to avoid missed commits, PR updates, or work items created mid-migration.\n\n- Trial runs do not require freezes and can be run anytime.\n\n\nBatching guidance:\n\n\n- Run trial batches back-to-back to shorten elapsed time; let teams validate results asynchronously.\n\n- Use your planned group/subgroup structure to define batches and respect API rate limits.\n\n\nRecommended steps:\n\n\n1. Create a test destination in GitLab for trials:\n\n\n - GitLab.com: create a dedicated group/namespace (for example, my-org-sandbox)\n\n - Self-managed: create a top-level group or a separate test instance if needed\n\n\n2. Prepare authentication:\n\n\n - Azure DevOps PAT with required scopes.\n\n - GitLab Personal Access Token with api and read_repository (plus admin access for file-based imports used by Congregate).\n\n\n3. Run trial migrations:\n\n\n - Repos only: use GitLab's built-in import (Repo by URL)\n\n - Repos + PRs/MRs and additional assets: use Congregate\n\n\n4. Post-trial follow-up:\n\n\n - Verify repo history, branches, tags; merge requests (if migrated), issues/epics (if migrated), labels, and relationships.\n\n - Check permissions/roles, protected branches, required approvals, runners/tags, variables/secrets, integrations/webhooks.\n\n - Validate pipelines (`.gitlab-ci.yml`) or converted pipelines where applicable.\n\n\n5. Ask users to validate functionality and data fidelity.\n\n6. Resolve issues uncovered during trials and update your runbooks.\n\n7. Network and security:\n\n\n - If your destination uses IP allow lists, add the IPs of your migration host and any required runners/integrations so imports can succeed.\n\n\n8. Run production migrations in waves:\n\n\n - Enforce change freezes in ADO during each wave.\n\n - Monitor progress and logs; retry or adjust batch sizes if you hit rate limits.\n\n\n9. Optional: remove the sandbox group or archive it after you finish.\n\n\n\u003Cfigure class=\"video_container\">\n \u003Ciframe src=\"https://www.youtube.com/embed/ibIXGfrVbi4?si=ZxOVnXjCF-h4Ne0N\" frameborder=\"0\" allowfullscreen=\"true\">\u003C/iframe>\n\u003C/figure>\n\n\n## Terminology reference for GitLab and Azure DevOps\n\n| GitLab | Azure DevOps | Similarities & Key Differences |\n| ---------------------------------------------------------------- | -------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| Group | Organization | Top-level namespace, membership, policies. ADO org contains Projects; GitLab Group contains Subgroups and Projects. |\n| Group or Subgroup | Project | Logical container, permissions boundary. ADO Project holds many repos; GitLab Groups/Subgroups organize many Projects. |\n| Project (includes a Git repo) | Repository (inside a Project) | Git history, branches, tags. In GitLab, a \"Project\" is the repo plus issues, CI/CD, wiki, etc. One repo per Project. |\n| Merge Request (MR) | Pull Request (PR) | Code review, discussions, approvals. MR rules include approvals, required pipelines, code owners. |\n| Protected Branches, MR Approval Rules, Status Checks | Branch Policies | Enforce reviews and checks. GitLab combines protections + approval rules + required status checks. |\n| GitLab CI/CD | Azure Pipelines | YAML pipelines, stages/jobs, logs. ADO also has classic UI pipelines; GitLab centers on .gitlab-ci.yml. |\n| .gitlab-ci.yml | azure-pipelines.yml | Defines stages/jobs/triggers. Syntax/features differ; map jobs, variables, artifacts, and triggers. |\n| Runners (shared/specific) | Agents / Agent Pools | Execute jobs on machines/containers. Target via demands (ADO) vs tags (GitLab). Registration/scoping differs. |\n| CI/CD Variables (project/group/instance), Protected/Masked | Pipeline Variables, Variable Groups, Library | Pass config/secrets to jobs. GitLab supports group inheritance and masking/protection flags. |\n| Integrations, CI/CD Variables, Deploy Keys | Service Connections | External auth to services/clouds. Map to integrations or variables; cloud-specific helpers available. |\n| Environments & Deployments (protected envs) | Environments (with approvals) | Track deploy targets/history. Approvals via protected envs and manual jobs in GitLab. |\n| Releases (tag + notes) | Releases (classic or pipelines) | Versioned notes/artifacts. GitLab Release ties to tags; deployments tracked separately. |\n| Job Artifacts | Pipeline Artifacts | Persist job outputs. Retention/expiry configured per job or project. |\n| Package Registry (NuGet/npm/Maven/PyPI/Composer, etc.) | Azure Artifacts (NuGet/npm/Maven, etc.) | Package hosting. Auth/namespace differ; migrate per package type. |\n| GitLab Container Registry | Azure Container Registry (ACR) or others | OCI images. GitLab provides per-project/group registries. |\n| Issue Boards | Boards | Visualize work by columns. GitLab boards are label-driven; multiple boards per project/group. |\n| Issues (types/labels), Epics | Work Items (User Story/Bug/Task) | Track units of work. Map ADO types/fields to labels/custom fields; epics at group level. |\n| Epics, Parent/Child Issues | Epics/Features | Hierarchy of work. Schema differs; use epics + issue relationships. |\n| Milestones and Iterations | Iteration Paths | Time-boxing. GitLab Iterations (group feature) or Milestones per project/group. |\n| Labels (scoped labels) | Area Paths | Categorization/ownership. Replace hierarchical areas with scoped labels. |\n| Project/Group Wiki | Project Wiki | Markdown wiki. Backed by repos in both; layout/auth differ slightly. |\n| Test reports via CI, Requirements/Test Management, integrations | Test Plans/Cases/Runs | QA evidence/traceability. No 1:1 with ADO Test Plans; often use CI reports + issues/requirements. |\n| Roles (Owner/Maintainer/Developer/Reporter/Guest) + custom roles | Access levels + granular permissions | Control read/write/admin. Models differ; leverage group inheritance and protected resources. |\n| Webhooks | Service Hooks | Event-driven integrations. Event names/payloads differ; reconfigure endpoints. |\n| Advanced Search | Code Search | Full-text repo search. 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