[{"data":1,"prerenderedAt":793},["ShallowReactive",2],{"/en-us/blog/optimizing-the-value-exchange-reduce-waste-to-increase-flow":3,"navigation-en-us":37,"banner-en-us":437,"footer-en-us":447,"blog-post-authors-en-us-Gabe Weaver":689,"blog-related-posts-en-us-optimizing-the-value-exchange-reduce-waste-to-increase-flow":703,"assessment-promotions-en-us":744,"next-steps-en-us":783},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":36},"blogPosts/en-us/blog/optimizing-the-value-exchange-reduce-waste-to-increase-flow.yml","Optimizing The Value Exchange Reduce Waste To Increase Flow",[7],"gabe-weaver",null,"unfiltered",{"slug":11,"featured":12,"template":13},"optimizing-the-value-exchange-reduce-waste-to-increase-flow",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Optimizing The Value Exchange: Reduce Waste To Increase Flow","Part two of a pragmatic, business-driven guide to help teams transition from fixating on output to optimizing the value exchange with their customers.",[18],"Gabe Weaver","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749672713/Blog/Hero%20Images/post-2-cover.jpg","2019-12-16","If you're new to the series, checkout Part 1: [A Gentle(ish)\nIntroduction](/blog/optimizing-the-value-exchange-a-gentle-introduction/)\n\n_Reading time: 13 minutes, 32 seconds_\n\n## Learning To Identify Waste\n\n> “Systems behavior is particularly sensitive to the goals of feedback\nloops. If the goals-the indicators of satisfaction of the rules - are defined inaccurately or incompletely, the system may obediently work to produce a result that is not really intended or wanted.” -- [Thinking In\nSystems](https://www.amazon.com/Thinking-Systems-Donella-H-Meadows/dp/1603580557)\n\nSince following the Improvement Kata has gotten the group this far, they decide to use another concept from the Toyota Production System (TPS) -- waste reduction. To increase flow through the value delivery system, they start by looking at steps in the workflow that do not add any intrinsic value. TPS categorizes waste into 8 distinct types:\n\n- **Talent:** Underutilizing people's talents, skills, and knowledge.\n\n- **Inventory:** Excess products and materials not being processed.\n\n- **Motion:** Unnecessary movements by people.\n\n- **Waiting:** Wasted time waiting for the next step in a process. \n\n- **Transportation:** Unnecessary movements of products and materials.\n\n- **Defects:** Efforts caused by rework, scrap, and incorrect information.\n\n- **Overproduction:** Production that is more than needed or before it is\nneeded.\n\n- **Overprocessing:** More work or higher quality than is required by the\ncustomer. \n\nWith this in mind, the working group pulls up their value stream map and immediately identifies a common theme across stages in the flow - some stages have a high amount of time spent waiting. The group quickly adds this up to understand the impact.\n\n![Acme's Value Stream Map - Time waiting](https://about.gitlab.com/images/blogimages/optimizing-the-value-exchange/d2.png)\n\nIn total, work items spend ~85 days waiting between queues or waiting for additional work needed from other parts of the R&D team. The group is speechless as they realize that it only takes around 3 days of actual labor to move a work item through the value delivery stream. They decide this is the right place to start and prioritize their efforts based on which cycles have the highest amount of time in waiting:\n\n1. **Build** - 863 hours in a queue, 613 hours once a work item has started.\n\n2. **Plan** - 362 hours once a work item has started.\n\n3. **Review** - 27 hours in a queue, 95 hours once a work item has started.\n\n4. **Canary** - 73 hours in a queue.\n\n5. **Prod** - 22 hours in a queue.\n\n6. **Staging** - 1 hour in a queue.\n\nThe group is excited about their discovery and is eager to share it with the engineering team. When they sit down and show them the numbers, the engineers are surprised. The conversation continues with the engineers talking through what it looks like for a single engineer in a typical week.\nThe working group captures it in a diagram and confirms with the engineers that it is representative of the conversation.\n\n![Sequence\nDiagram](https://about.gitlab.com/images/blogimages/optimizing-the-value-exchange/sequence-diagram.png)\n\nThe group asks the engineers how they feel about this workflow. They're generally supportive as they don't see an alternative to this despite it being extremely heavy on the context switching. The working group still needs to figure out how to reduce the waiting time for work items in this stage and eliminated unneccessary waiting is a logical place to start. They present their target condition and ask the engineers for suggestions. The engineers feel strongly that the code review process is imperative to maintain code quality. One engineer suggests the team could try pair programming. He heard that it can increase code quality. It would also remove the Review stage entirely as two people contribute to the same work item until it is fully completed.\n\nThey debated back and forth. Many felt that it would slow everything down even more because they would only be able to do half the work they had been doing up to that point. The engineer who brought it up in the first place pointed to an a [experiment](https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20030012934.pdf)\nwhere NASA engineers completed two like-size projects - one as they normally would and one strictly following [Extreme\nProgramming](http://www.extremeprogramming.org/); which includes a mix of practices like pair programming, collective code ownership, TDD, and small iterations. He presented the team with the following data:\n\n| | Non-XP Projet | XP Project |\n| --- | --- | --- |\n| LOC/Hour | 24 | 27 |\n| Util/test LOC | 0 | 1,135 |\n| Prod LOC | 2,144 | 912 |\n| Total LOC | 2,144 | 2,545 |\n\nHe explained that part of the reason why the team was able to produce less production code at a faster overall rate was due to the pair pushing each other not to cut corners by focusing on quality; ruthlessly refactoring code as they went. He also discussed the concept of the driver and navigator roles, and how that led to better architecture decisions the first time around. The engineers were skeptical but were willing to give it a try.\nTogether, everyone agreed that the engineers would run an experiment to try pair programming for the upcoming quarter. They also agreed to only work on one item from start to merged before picking up the next item.\n\n## Increased flow does not equal increased value\n\nAt the end of the quarter, the working group got together to review an updated value stream map. \n\n![Acme's Value Stream Map -\nUpdated](https://about.gitlab.com/images/blogimages/optimizing-the-value-exchange/d3.png)\n\nThe work in progress limits and pair programming dramatically improved flow through the value stream. Lead time dropped to 893 hours or 37 days. A 57% reduction was much better than the working group had anticipated. The books for the quarter weren't closed, but the working group knew it was going to be a huge win. They eagerly shared the results with the executive team.\n\nAs the quarterly financials were released, the group got a call from the\nCFO. He was disappointed. He had expected to not only achieve the 5% incremental revenue growth but do much better given the results of the experiment. The working group was stunned. Revenue growth only increased from 5% to 9%. Despite missing the mark, the executives agreed to continue sponsoring the working group as it did produce tangible results and business outcomes. \n\n**Financials**\n\n| | Previous | Current | \n| --- | --- | --- |\n| Revenue | 50.9 | 55.4 |\n| COGS | 4.6 | 5.0 |\n| **Net Sales** | **46.3** | **50.1** |\n| *Margin* | *91%* | *91%* |\n|  |  |  |\n| R&D | 22.7 | 23.1 | \n| Marketing | 7.4 | 8 |\n| Sales | 21.2 | 22.9 |\n| G&A | 2.9 | 3.8 |\n| **Total OpEx** | **54.2** | **57.8** |\n| |  |  |\n| **Net Profit** | **-7.9** | **-7.3** |\n| *Profit Margin* | *-16%* | *-13%* |\n\n\u003Cbr>\n\n**Value Delivery Economics**\n\n| | Previous | Current |\n| --- | --- | --- | \n| Items Delivered | 3,131 | 5,002 |\n| Lead Time | 88 | 37 |\n| Cost Per Item Delivered | 7,250 | 4,617 |\n\nThey combed through the financials and productivity metrics to figure out what had happened. The value delivery system had become much more efficient.\nThe cost per item delivered decreased. What went wrong? They circled back to where they started with the sales team to ask for an update. The sales team reported that they still felt like speed on delivering customer requests was moving slowly. It had gotten a little bit better in a few areas, but overall it was having a nominal impact on fixing the problem of declining up-sell rates. Determined to get to the bottom of things, the working group decides to dig a bit deeper into the kind of work flowing through the R&D value stream. \n\nThis is what they discovered upon further investigation into the breakdown on types of work items R&D was delivering:\n\n| | Q2 | Q3 | Q4 | Q1 |\n| --- | --- | --- | --- | --- |\n| Feature | 63% | 46% | 38% | 44% |\n| Defect | 13% | 22% | 29% | 36% |\n| Technical Debt | 24% | 32% | 33% | 20% |\n\nA light bulb clicked. No wonder the sales team felt like things were moving slowly. The amount of new value being delivered to customers was near an all-time low. It was certainly better than the previous quarter thanks to the improvement in lead time, but this trend was concerning to the group. \n\nAs they followed the same path from the first time around, they connected with the R&D team to ask questions. After presenting the work item breakdown by type, R&D was not surprised at all. They look at this ratio quite often.\nWhen the group asked them why they hadn't surfaced this earlier, they explained that the pace of constant feature prioritization over the last several years gave the team little extra time to refactor parts of the codebase that were critical to the value delivery system. \n\nIn continuing to listen intently, the team conveyed that they had brought this up on several occasions. Upper management insisted that the team focus on creating new value to fuel the growth targets set by the company leaders.\nThe team also explained that because the company has SLAs with customers, they have had to prioritize severe defects over new features; leaving little time for anything else. \n\nA product manager stepped forward and openly confessed that he regularly makes pacts with his engineering manager to repay technical debt in short order if they would do whatever it took to get the feature shipped. He said they rarely followed through to go back and clean up technical debt.\nEveryone in the meeting confirmed that this was an acceptable practice. The working group started to see the long term consequences that are so easily masked by strong financial growth. As the R&D team cut corners to ship features, technical debt mounted. Unable to take the time to address it, defect count had started increasing at a consistent rate. The R&D team now had to split focus across three different areas - trying to keep the lights on, delivering new value, and trying to find time to repay the technical debt. \n\n![Technical Debt\nDiagram](https://about.gitlab.com/images/blogimages/optimizing-the-value-exchange/tech-debt.jpg)\n\nDespite this revelation, the working group was still responsible for delivering on their target condition. Now it was on them to convince leadership to take technical debt seriously. As they mulled over ways to present this, they asked for a report of all the inventory that was waiting to be produced. For this to resonate with the executives, they needed to communicate it in a way that provided justifiable business reasons to throttle back on feature development. They calculated the cost per quarter that the company was spending on work that did not add intrinsic customer value or positively reinforce the Value Exchange System.\n\n| | Type | Quarterly Cost |\n| --- | --- | --- |\n| Features | 38% | 8.8 | \n| Defects | 22% | 5.1 | \n| Tech Debt | 40% | 9.2 | \n\nThe company was investing $14.3 million in activities that did not have any tangible impact on driving revenue growth. This was something the leadership team could not ignore. As they presented their case, the executives were concerned that slowing down on feature development in any way would adversely impact revenue growth. The working group talked around this by helping them understand that slowing down for a little bit right now would enable them to accelerate much faster as they would be able to more than double their investment in value-adding features once defects and technical debt were in check. The leadership agreed to the working group's proposal. \n\nOver the next few quarters, R&D invested heavily in paying down technical debt and reducing defects. They also made optimizations to the value stream and were able to replace both the staging and canary environments by deploying directly to production behind feature flags. Product managers were able to test things out in production and as soon as a feature was ready for release, they clicked a button and it would become incrementally available to everyone. New defects were at an all-time low and the team was confident all of the efforts would produce huge dividends. \n\nThe working group double-checked the value stream in anticipation of reviewing the quarterly financials that contained the first full quarter where the team spent 80% of their time on value-adding features. \n\n![Acme's Value Stream Map - 80% features%](https://about.gitlab.com/images/blogimages/optimizing-the-value-exchange/d4.png)\n\n**Value Delivery Economics**\n\n| | W/ Tech Debt | 80% Features |\n| --- | --- | --- | \n| Items Delivered | 5,002 | 11,467 |\n| Lead Time | 37 | 11.9 |\n| Cost Per Item Delivered | 4,617 | 2,014 |\n\nThey were pleased with their value stream. It was fine-tuned, waste-free, and incredibly efficient.  Leads times were hovering around 286 hours (11.9 days). The company had continued to invest in scaling the R&D team and they were able to churn out an astonishing 11,467 work items. But then came the financials. \n\n| | W/ Tech Debt | Paying It Down | 80% Features | \n| --- | --- | --- | --- |\n| Revenue | 55.4 | 57.7 | 68.04 |\n| COGS | 5.0 | 5.2 | 6.1 |\n| **Net Sales** | **50.5** | **52.5** | **61.9** |\n| *Margin* | *91%* | *91%* | *91%* |\n|  |  |  | |\n| R&D | 23.1 | 25.3 | 27.2 | \n| Marketing | 8 | 8.9 | 9.6 |\n| Sales | 22.9 | 24 | 25.6 |\n| G&A | 3.8 | 5.1 | 6.4 |\n| **Total OpEx** | **57.8** | **63.3** | **68.8** |\n| |  |  | |\n| **Net Profit** | **-7.3** | **-10.8** | **-6.9** |\n| *Profit Margin* | *-13%* | *-19%* | *-10%* |\n\nThey only improved revenue growth rates by 9 points, from 9% to 18%. The working group was surprised the CFO hadn't called. When they checked their calendars, a week-long onsite meeting had been scheduled with heads from every division at the company. It wasn't all too surprising. They continued to fail to meet the mark, but could not figure out why. The cost per work item had been reduced by 72%. Investment in value-adding work items was at an unprecedented all-time high. The value delivery stream was a well-oiled machine, but growth rates weren't responding.\n\nAs the working group prepared to meet their fate, they decided to conduct a retrospective to try to gain an understanding of where they went wrong.\nDuring the conversation, one of the group members reminded the team of the earlier conversation with R&D discussing success metrics; where R&D recounted that Acme had never really figured out how to more formally tie business outcomes to the output they were creating. The group didn't know the answer, but they agreed to spend some time before the on-site to explore the topic.\n\nThe group talked to peers at other companies and read as many books as they could get their hands on. One of them was [The\nGoal](https://www.amazon.com/Goal-Process-Ongoing-Improvement/dp/0884271951/ref=asc_df_0884271951/?tag=hyprod-20&linkCode=df0&hvadid=312175933381&hvpos=1o1&hvnetw=g&hvrand=10925659369993672127&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9031326&hvtargid=pla-434522630098&psc=1&tag=&ref=&adgrpid=60258871817&hvpone=&hvptwo=&hvadid=312175933381&hvpos=1o1&hvnetw=g&hvrand=10925659369993672127&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9031326&hvtargid=pla-434522630098)\nby Eliyahu Goldratt, a story much similar to the journey that the working group had been on. As the group discussed the book, they finally realized the fatal flaw they had made -- they had spent all of their time fixated on trying to achieve the local maximum of R&D instead of aligning the feedback loops between the value delivery system and the value capture system. \n\nThe most effective way to do this is by focusing on the efficiency of the\nValue Exchange System as a whole. As the group learned, this can be accomplished by measuring the Value Exchange System's throughput. It's important to note that this kind of throughput should not be confused with the definition in Kanban (work output), but rather the rate at which a system generates revenue through sales. The group had achieved a new level of alignment...almost. To start measuring this, they first needed to learn more about throughput accounting. \n\n## Throughput Accounting 101\n\n> “The most marvelous characteristic of some complex systems is their\nability to learn, diversify, complexify, and evolve…Like resilience, self-organization is often sacrificed for purposes of short-term productivity and stability. Productivity and stability are the usual excuses for turning creative human beings into mechanical adjuncts to production processes.” -- [Thinking In\nSystems](https://www.amazon.com/Thinking-Systems-Donella-H-Meadows/dp/1603580557)\n\nIn stark contrast to cost-based accounting, [throughput accounting](https://en.wikipedia.org/wiki/Throughput_accounting) prioritizes value creation over cost-cutting. It consists of [three primary metrics](https://www.amazon.com/Scaling-Lean-Mastering-Metrics-Startup/dp/1101980524):\n\n- **Throughput** is the rate at which monetizable value is generated from a\ncompany's customers over their lifetime minus any variable costs such as the cost of customer acquisition. \n\n- **Inventory** represents all of the money a company has invested into the\nValue Exchange System including current product capabilities, unfinished goods (requirements, features, job stories, etc.), equipment, and infrastructure. \n\n- **Operating Expenses** are the costs expended turning inventory into\nthroughput. This includes research and development, sales, and general expenses. \n\nThese metrics are then built up to determine the financial health of a given\nValue Exchange System:\n\n- **Throughput (T)** = Lifetime Value (LTV) - Cost of Customer Acquisition\n(COCA)\n\n- **Profit (P)** = Total Throughput (T) - Operating Expenses (OE)\n\n- **Return On Investment (ROI)** = Profit (P) / Inventory (I)\n\nHere is where the differences between cost-based accounting and throughput accounting start to manifest. As Ash Maurya, points out in Scaling Lean:\n\n> Cost-based accounting places more emphasis on the right-hand side of the\nprofit equation — decrease operating expenses. It focuses on scalable efficiency and squeezing out costs — especially labor costs. This typically manifests itself as policies requiring detailed weekly timesheets broken down by task, as well as downsizing, outsourcing, and other cost-reducing measures.\n\n> It is much more powerful to try to affect the left-hand side of the profit\nequation — increase throughput — because cost-cutting has a theoretical limit of zero. Increasing throughput has no theoretical upper limit. You can find ways to add more value to an existing product, build more add-on products, or expand the market — provided, of course, that these efforts lead to a positive return on investment.\n\nWhat's also important about looking at the Value Exchange System through the lens of throughput accounting is that it provides the preferred order for where to focus optimization efforts:\n\n1. Throughput\n\n1. Decreasing inventory\n\n1. Reducing operating expenses\n\n## The Universal Goal\n\nAs Eliyahu Goldratt, creator of throughput accounting and author of [The\nGoal](https://en.wikipedia.org/wiki/The_Goal_(novel)), frames it:\n\n> The universal goal of every business is to increase throughput while\nminimizing inventory and operating expenses provided doing that doesn't degrade throughput\n\nThe working group was starting to think differently. Throughput accounting provides a simple, but powerful decision support framework. Every target condition should map to one of three desired outcomes in the Value Exchange\nSystem - increase throughput (T), reduce investment (I), or reduce operating expenses (OE). It's also important to point out that throughput accounting does not use the balance sheet to [stash deferred expenses](https://www.scienceofbusiness.com/throughput-accounting/) to be realized later. This is a practice that many companies use to manipulate the income statement depending on the kind of story they want to tell. This ultimately masks the true health of the Value Exchange System. \n\nWhile excited about what they are learning, the working group was still a bit stumped about how to implement it within Acme in a way that would help drive improved revenue growth and profitability. They weren't able to formalize a proposal in time for the onsite, but they were now equipped with some better data points around why they had failed and planned to share the news with the executive group.\n\nContinue reading:\n\n- Part 1: [A Gentle(ish)\nIntroduction](/blog/optimizing-the-value-exchange-a-gentle-introduction/)\n\n- Part 2 : Reduce Waste To Increase Flow\n\n- Part 3 (Next): [The Compounding Value Of Shorter Feedback\nLoops](/blog/optimizing-the-value-exchange-the-compounding-value-of-shorter-feedback-loops/)\n\n***Giving credit where it is due:** Throughput Accounting and the Theory Of\nConstraints was originally pioneered by Eliyahu Goldratt. 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I’m Jacie Bandur. I completed GitLab’s CEO Shadow program from 2021-04-26 through 2021-05-07. It was a really enlightening experience. I generally work in Learning and Development and consider myself a lifelong learner. I can’t even explain how much I learned in such a short about of time. I learned a lot about the business. I learned a lot about the product. But learned even more about the importance of iteration in everything we do.\n\n### Qualifications to Participate\n\nI wanted to start this off with touching on qualifications to participate in the program.\n\nI am the type of person that has gone through most of my life thinking I’m not qualified for things. I’m not qualified for that job, that promotion, that program. The list goes on and on.\n\nWhen I saw the [CEO Shadow program](/blog/ceo-shadow-impressions-takeaways/) kick off in 2019, I really wanted to participate. I was a little intimidated. Who wouldn’t be, spending 2 weeks with the CEO of any company? But time passed and all the sudden it was 2021 and I had not taken any steps to participating in the program.\n\nIf you are sitting there waiting for someone to tell you that you are qualified to participate in this program, I’m not big on giving “pep talks,” but here’s me telling you - You are qualified for this program. There’s never going to be a good or perfect time to do it. Tell your manager you want to do the CEO Shadow program. Stop waiting. Sign up today.\n\nNote: Take a look at the [eligibility](https://handbook.gitlab.com/handbook/ceo/shadow/#eligibility) section of the CEO Shadow page for more information on signing up.\n\n### Pre-Program Tips\n\nThere are many things recommended for shadows to do pre-program outlined on the CEO Shadow handbook page. As I was going through the program there were things that I thought helped me (or would have helped me).\n\nHere are my top 6 recommendations:\n\n1. Make sure your team knows you will be unavailable for 2 weeks. This isn’t a program that can or should be done alongside your normal day to day work. I found catching up from the 2 weeks away kind of difficult because I was trying to keep up on what was going on and I had a bunch of half done things.\n1. Talk with people who have done the shadow program - schedule at least 3 coffee chats with CEO Shadow Alumni.\n1. Have food that is easy to eat quickly. Sid’s meetings are back to back most days, so you will have small amounts of time to eat throughout the day. Sid does eat during calls, which you are welcome to do, too, but if you are taking notes, it is difficult to eat. And this will make you realize why speedy meetings are so important!\n1. Listen to the [Executive Leadership LinkedIn Learning course](https://www.linkedin.com/learning/executive-leadership/).\n1. Be prepared to ask questions. When doing the program virtually, there isn’t a ton of time for asking questions, so when one would come up, I would add it to a note on my computer and ask if there was ever time with just the shadows and Sid.\n1. Take at least 1 day off after the program. Take even a couple of days off if you can! This is recommended on the handbook page, but I can’t stress this enough.\n\n\n### Takeaways\n\n**Group Conversations**\n\nI’ve been at GitLab for almost 4 years. When I joined, I made it a point to attend as many GC’s as I could. I had gotten out of the habit of attending Group Conversations. After attending them again for 2 weeks, I realized how important they are to understand better what is going on across the business. Everything in the organization is so intertwined. It’s helpful to understand what other teams are working on and succeeding in.\n\n**Feedback**\n\nWe should all be giving and receiving feedback often. We have a whole [handbook page on giving and receiving feedback](https://handbook.gitlab.com/handbook/people-group/guidance-on-feedback/). Read the handbook page and watch the videos, as well. Practice giving feedback. I recommend using the [1-1 agenda](https://handbook.gitlab.com/handbook/leadership/1-1/suggested-agenda-format/) Sid uses, because Feedback is an essential piece of that agenda, and it makes feedback more of a routine thing.\n\n**Biggest Takeaway**\n\nWe have an incredible team here at GitLab, from Engineering to Product to Sales to People and all the groups in between. There are so many great ideas. I observed the constant reinforcement by Sid to start with something small and build on it. You can ALWAYS make something more complex. It’s hard to go back to something more simple when you start with something complex.\n\nA couple of quotes that I heard from Sid during the program that reinforced this point:\n\n- “Every complex system evolves from a simple system that worked.”\n- “It’s very clear what is the simple solution. We can always make it more complicated as we go on.”\n\nI know they are very similar, but they happened in different meetings on different days, so the point was reinforced repeatedly.\n\nDuring the program, I reflected on the projects that I’am working on. How many of them am I trying to do too much on before releasing. Probably all of them. When I’m working on projects in the future, I will break them down into smaller, more doable chunks. Iteration is hard - it’s a skill to be practicing constantly.\n\n\n### Overall\n\nOverall, the program was really insightful and impactful. If you haven’t participated in it yet, I cannot encourage you enough to do so!\n",{"slug":714,"featured":12,"template":13},"ceo-shadow-recap",{"content":716,"config":728},{"title":717,"description":718,"authors":719,"heroImage":721,"date":722,"body":723,"category":9,"tags":724},"Why I love contributing to GitLab","Making small meaningful changes is what it's all about.",[720],"Austin Regnery","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749679501/Blog/Hero%20Images/new-feature.png","2021-05-11","It was mid-morning on a Tuesday in February, and I had 10 minutes in between meetings. So I decided to try and solve a pain point of mine.\nYou see, I had to memorize this HTML snippet to create a collapsible section in GitLab Issue descriptions and comments, but I kept forgetting it. Was it `summary` or `section`? I could never remember.\n```html\n\u003Cdetails>\n\u003Csummary>Insert Title\u003C/summary>\nHidden content\n\u003C/details>\n```\nEven though it is not vanilla Markdown, GitLab knows how to interpret some HTML. I used this formatting trick fairly often since full-page screenshots can occupy a lot of screen space, which leads to excessive scrolling.\nSo I decided to poke around our codebase to see how the other Markdown shortcuts worked. To my surprise, it was pretty straightforward. Each shortcut had a simple text input that mapped to each button. This implementation was simple to replicate since I just needed to copy/paste and replace a few words.\n![Image of Vue and Haml files with editor shortcuts](https://about.gitlab.com/images/blogimages/why-i-love-contributing-to-gitlab/vue-haml.png){: .shadow}\nThe Vue and Haml files with the new shortcut\n\nI started a branch and began hacking away at the code. Now, I would never call myself a Software Engineer, but I like to try and make things from time to time. I was able to add a new shortcut to the toolbar to insert this code snippet for me in less than 10 minutes. No more memorizing! Making contributions like this is what makes working at GitLab so special.\nNow, it wasn't ready for production, but I at least had something that worked. I shared it with my UX colleagues in Slack, and it started to gain traction with several up-votes and few constructive comments on how to make it better.\nWith the functionality flushed out, a few other designers helped me get a better icon added to our SVG library. Using clear iconography is critical for communicating information more clearly.\n| Initial Icon | Final Icon |\n| - | - |\n| ![SVG of chevron right icon](https://about.gitlab.com/images/blogimages/why-i-love-contributing-to-gitlab/chevron-right.svg) | ![SVG of details block icon](https://about.gitlab.com/images/blogimages/why-i-love-contributing-to-gitlab/details-block.svg) |\n\nThe last thing to do was resolve my failing tests, and I had several teammates help me do that.\n![Gif of the shortcut being used](https://about.gitlab.com/images/blogimages/why-i-love-contributing-to-gitlab/demo.gif)\n\nToday [this change](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/54938) merged! Now I solved a pain point for me and others. It took a few months to go from idea to production, but the effort was super low. I'd say the return on my initial investment, 10 minutes, is super high.\n> Having a direct impact on a product was never an option for me before joining GitLab.\n\n![Image of participants in the Merge Request](https://about.gitlab.com/images/blogimages/why-i-love-contributing-to-gitlab/participants.png)\n\n\nThank you to everyone that helped me deploy this\n",[725,726,727],"UX","product","AWS",{"slug":729,"featured":12,"template":13},"why-i-love-contributing-to-gitlab",{"content":731,"config":742},{"title":732,"description":733,"authors":734,"heroImage":736,"date":722,"body":737,"category":9,"tags":738},"Placebo Lines on the Pipeline Graph","Have you noticed the connecting lines missing on your pipelines lately? Here's why",[735],"Sam Beckham","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749679507/Blog/Hero%20Images/ci-cd.png","\n\n{::options parse_block_html=\"true\" /}\n\n\n\nHave you ever pressed the close door button on the elevator, in the hope that you'll save a few precious seconds?\nOr got frustrated at the person stood next to you at the cross-walk, neglecting to press the button?\nWell, maybe they know something you don't, or perhaps you know this already.\nMany buttons in our society lie to us.\n[David McRaney](https://youarenotsosmart.com/2010/02/10/placebo-buttons/) dubbed these, \"Placebo buttons\" and they're everywhere.\nThose elevator doors won't close any faster and the cross-walk button has no effect on the lights.\nThe only lights they control are the lights on the buttons themselves.\nThey give you the feedback you crave, but that's all they're doing.\n\nThese placebos aren't constrained to the physical world, they're prevalent in [UI design](/blog/the-evolution-of-ux-at-gitlab/) too.\nFrom literal placebo buttons like [YouTube's downvote](https://www.quora.com/Does-downvoting-a-comment-on-YouTube-even-do-anything), to more subtle effects like Instagram always [pretending to work](https://www.fastcompany.com/1669788/the-3-white-lies-behind-instagrams-lightning-speed), or progress bars that have a [fixed animation](https://www.theatlantic.com/technology/archive/2017/02/why-some-apps-use-fake-progress-bars/517233/).\nThey're everywhere if you know where to look.\n\nAt GitLab, we created a placebo of our own in one of our core features; the pipeline graph.\n\nThose of you who have used our pipeline graph, will be familiar with its appearance.\nThere's a series of jobs, grouped by stages, connected by a series of lines depicting the relationships between the jobs.\nBut these lines might be lying to you.\nThese lines are indiscriminately drawn between each job in a stage, regardless of their relationship.\nThese lines are placebos.\n\n![The old pipeline rendering with lines connecting every job in a stage](https://about.gitlab.com/images/blogimages/placebo-lines_old-graph.png)\n\nThis wasn't a problem to begin with.\nA basic pipeline has several jobs across a handful of stages.\nJobs in each stage would run parallel to each other, but each stage would run sequentially.\nIn the image shown above, all the jobs in the test stage would trigger at the same time. Once those jobs had finished, all the jobs in the build stage would trigger.\nWe used rudimentary CSS to draw lines connecting each job in one stage to each job in the next.\nThese lines weren't calculated based on their connections, but still reflected the story they were telling.\n\nSince the introduction of `needs` relationships in [v12.2](https://gitlab.com/gitlab-org/gitlab-foss/-/issues/47063), pipelines got a bit more complicated.\nNow you could configure a job in a later stage to trigger as soon as a job in an earlier stage completed.\nLooking at our old example, we could set the API deployment to run as soon as our spec tests passed.\nThis skips the remaining tests and the entire build stage, turning our lines into pretty little liars.\n\nWe had many internal discussions about these lines, and how to show the relationships between jobs.\nThere's the [`needs` visualization](https://docs.gitlab.com/ee/ci/directed_acyclic_graph/#needs-visualization), which does an excellent job of displaying these relationships, but the main pipeline graph was still inaccurate.\nFor the past few months, we've been [refactoring the pipeline graph](https://gitlab.com/gitlab-org/gitlab/-/issues/276949), giving it a new lease of life and fixing some of its issues along the way.\nOne of those issues were the faked lines.\nIn the new version, we can accurately draw lines between jobs.\nLines that actually depict the relationships jobs have with each other.\nNow the lines no-longer lie!\n\n![The newer pipeline graph showing the correct needs links between jobs](https://about.gitlab.com/images/blogimages/placebo-lines_new-graph.png)\n\nThe above image shows an unreleased version of the pipeline graph.\nYou can see the lines drawn between the jobs to show that the `deploy:API` job can start as soon as the `rspec` job is successful.\nSomething the old lines (shown earlier in this post) would have been unable to depict.\n\nOne unfortunate downside of this is that these lines can be quite expensive to calculate.\nThey're actual DOM nodes, drawn deliberately and placed precisely.\nOn smaller graphs this isn't a problem, but some of our initial tests have found pipelines with a potential 8000+ job connections.\nThat kind of calculation would grind the browser to a halt, and nobody wants that.\n\nAt GitLab, we believe in boring solutions.\nWe make the simple change that sets us on the path towards where we want to be.\nShip it, get feedback, and iterate.\nSo that's what we did.\nIn the first phase of this rollout, we shipped the new pipeline graph with no lines connecting the jobs.\nWe don't have to worry about the expensive calculations, and we still get to roll out the refactored pipeline graph.\n\n![The current (v13.11) pipeline graph showing no links between jobs](https://about.gitlab.com/images/blogimages/placebo-lines_current-graph.png)\n\nWe know some of you will miss them, but fear not.\nBoring solutions are just technical debt if you don't iterate on them.\nSo the [improved lines are coming](https://gitlab.com/groups/gitlab-org/-/epics/4509) in a future release, along with several other improvements to the pipeline graph.\nWe're already starting to roll out the new [Job Dependencies](https://gitlab.com/gitlab-org/gitlab/-/issues/298973) view which shows the jobs in a (much closer to) execution order.\nStay tuned for more updates, and watch [Sarah Groff Hennigh Palermo's talk](https://www.youtube.com/watch?v=R2EKqKjB7OQ) for the technical side of this effort and a deeper dive into some of the decisions we made.\n",[739,740,23,741],"CI","frontend","design",{"slug":743,"featured":12,"template":13},"placebo-lines-on-the-pipeline-graph",{"promotions":745},[746,760,771],{"id":747,"categories":748,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[749],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":753,"config":754},"Get your AI maturity score",{"href":755,"dataGaName":756,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":758},{"src":759},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":761,"categories":762,"header":763,"text":751,"button":764,"image":768},"devops-modernization",[726,557],"Are you just managing tools or shipping innovation?",{"text":765,"config":766},"Get your DevOps maturity score",{"href":767,"dataGaName":756,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":772,"categories":773,"header":775,"text":751,"button":776,"image":780},"security-modernization",[774],"security","Are you trading speed for security?",{"text":777,"config":778},"Get your security maturity score",{"href":779,"dataGaName":756,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":784,"blurb":785,"button":786,"secondaryButton":791},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":787,"config":788},"Get your free trial",{"href":789,"dataGaName":48,"dataGaLocation":790},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":493,"config":792},{"href":52,"dataGaName":53,"dataGaLocation":790},1772652074872]