Tag Archives: design-systems

Design system annotations, part 2: Advanced methods of annotating components

Post Syndicated from Jan Maarten original https://github.blog/engineering/user-experience/design-system-annotations-part-2-advanced-methods-of-annotating-components/


In part one of our design system annotation series, we discussed the ways in which accessibility can get left out of design system components from one instance to another. Our solution? Using a set of “Preset annotations” for each component with Primer. This allows designers to include specific pre-set details that aren’t already built into the component and visually communicated in the design itself. 

That being said, Preset annotations are unique to each design system — and while ours may be a helpful reference for how to build them — they’re not something other organizations can utilize if you’re not also using the Primer design system. 

Luckily, you can build your own. Here’s how. 

How to make Preset annotations for your design system

Start by assessing components to understand which ones would need Preset annotations—not all of them will. Prioritize components that would benefit most from having a Preset annotation, and build that key information into each one. Next, determine what properties should be included. Only include key information that isn’t conveyed visually, isn’t in the component properties, and isn’t already baked into a coded component. 

The start of a list of Primer components with notes for those which need Preset annotations. There are notes pointing to ActionBar, ActionMenu, and Autocomplete with details about what information should be documented in their Preset.

Prioritizing components

When a design system has 60+ components, knowing where to start can be a challenge. Which components need these annotations the most? Which ones would have the highest impact for both design teams and our users? 

When we set out to create a new set of Preset annotations based on our proof of concept, we decided to use ten Primer components that would benefit the most. To help pick them, we used an internal tool called Primer Query that tracks all component implementations across the GitHub codebase as well as any audit issues connected to them. Here is a video breakdown of how it works, if you’re curious. 

We then prioritized new Preset annotations based on the following criteria:

  1. Components that align to organization priorities (i.e. high value products and/or those that receive a lot of traffic).
  2. Components that appear frequently in accessibility audit issues.
  3. Components with React implementations (as our preferred development framework).
  4. Most frequently implemented components. 

Mapping out the properties

For each component, we cross-referenced multiple sources to figure out what component properties and attributes would need to be added in each Preset annotation. The things we were looking for may only exist in one or two of those places, and thus are less likely to be accounted for all the way through the design and development lifecycle. The sources include:

Component documentation on Primer.style

Design system docs should contain usage guidance for designers and developers, and accessibility requirements should be a part of this guidance as well. Some of the guidance and requirements get built into the component’s Figma asset, while some only end up in the coded component. 

Look for any accessibility requirements that are not built into either Figma or code. If it’s built in, putting the same info in the Preset annotation may be redundant or irrelevant.

Coded demos in Storybook 

Our component sandbox helped us see how each component is built in React or Rails, as well as what the HTML output is. We looked for any code structure or accessibility attributes that are not included in the component documentation or the Figma asset itself—especially when they may vary from one implementation to another. 

Component properties in the Figma asset library

Library assets provide a lot of flexibility through text layers, image fills, variants, and elaborate sets of component properties. We paid close attention to these options to understand what designers can and can’t change. Worthwhile additions to a Preset Annotation are accessibility attributes, requirements, and usage guidance in other sources that aren’t built into the Figma component. 

Other potential sources 

  • Experiences from team members: The designers, developers, and accessibility specialists you work with may have insight into things that the docs and design tools may have missed. If your team and design system have been around for a while, their insights may be more valuable than those you’ll find in the docs, component demos, or asset libraries. Take some time to ask which components have had challenging bugs and which get intentionally broken when implemented.
  • Findings from recent audits: Design system components themselves may have unresolved audit issues and remediation recommendations. If that’s the case, those issues are likely present in Storybook demos and may be unaccounted for in the component documentation. Design system audit issues may have details that both help create a Preset annotation and offer insights about what should not be carried over from existing resources.

What we learned from creating Preset annotations

Preset annotations may not be for every team or organization. However, they are especially well suited for younger design systems and those that aren’t well adopted. 

Mature design systems like Primer have frequent updates. This means that without close monitoring, the design system components themselves may fall out of sync with how a Preset annotation is built. This can end up causing confusion and rework after development starts, so it may be wise to make sure there’s some capacity to maintain these annotations after they’ve been created. 

For newer teams at GitHub, new members of existing teams, and team members who were less familiar with the design system, the built-in guidance and links to documentation and component demos proved very useful. Those who are more experienced are also able to fine-tune the Presets and how they’re used.

If you don’t already have extensive experience with the design system components (or peers to help build them), it can take a lot of time to assess and map out the properties needed to build a Preset. It can also be challenging to name a component property succinctly enough that it doesn’t get truncated in Figma’s properties panel. If the context is not self-evident, some training or additional documentation may help.

It’s not always clear that you need a Preset annotation

There may be enough overlap between the Preset annotation for a component and types of annotations that aren’t specific to the design system. 
For example, the GitHub Annotation Toolkit has components to annotate basic <textarea> form elements in addition to a Preset annotation for our <TextArea> Primer component:

Comparison between a Form Element annotation for the textarea HTML element and a Preset annotation for the TextArea Primer component.

In many instances, this flexibility may be confusing because you could use either annotation. For example, the Primer <TextArea> Preset has built-in links to specific Primer docs, and while the non-Preset version doesn’t, you could always add the links manually. While there’s some overlap between the two, using either one is better than none. 

One way around this confusion is to add Primer-specific properties to the default set of annotations. This would allow you to do things like toggle a boolean property on a normal Button annotation and have it show links and properties specific to your design system’s button component. 

Our Preset creation process may unlock automation

There are currently a number of existing Figma plugins that advertise the ability to scan a design file to help with annotations. That being said, the results are often mixed and contain an unmanageable amount of noise and false positives. One of the reasons these issues happen is that these public plugins are design system agnostic.

Current automated annotation tools aren’t able to understand that any design system components are being used without bespoke programming or thorough training of AI models. For plugins like this to be able to label design elements accurately, they first need to understand how to identify the components on the canvas, the variants used, and the set properties. 

A Figma file showing an open design for Releases with an expanded layer tree highlighting a Primer Button component in the design. To the left of the screenshot are several git-lines and a Preset annotation for a Primer Button with a zap icon intersecting it. The git-line trails and the direction of the annotation give the feeling of flying toward the layer tree, which visually suggests this Primer Button layer can be automatically identified and annotated.

With that in mind, perhaps the most exciting insight is that the process of mapping out component properties for a Preset annotation—the things that don’t get conveyed in the visual design or in the code—is also something that would need to be done in any attempt to automate more usable annotations. 

In other words, if a team uses a design system and wants to automate adding annotations, the tool they use would need to understand their components. In order for it to understand their components well enough to automate accurately, these hidden component properties would need to be mapped out. The task of creating a set of Preset annotations may be a vital stepping stone to something even more streamlined. 

A promising new method: Figma’s Code Connect 

While building our new set of Preset annotations, we experimented with other ways to enhance Primer with annotations. Though not all of those experiments worked out, one of them did: adding accessibility attributes through Code Connect. 

Primer was one of the early adopters of Figma’s new Code Connect feature in Dev Mode. Says Lukas Oppermann, our staff systems designer, “With Code Connect, we can actually move the design and the code a little bit further apart again. We can concentrate on creating the best UX for the designers working in Figma with design libraries and, on the code side, we can have the best developer experience.” 

To that end, Code Connect allows us to bypass much of our Preset annotations, as well as the downsides of some of our other experiments. It does this by adding key accessibility details directly into the code that developers can export from Figma.

GitHub’s Octicons are used in many of our Primer components. They are decorative by default, but they sometimes need alt text or aria-label attributes depending on how they’re used. In the IconButton component, that button uses an Octicon and needs an accessible name to describe its function. 

When using a basic annotation kit, this may mean adding stamps for a Button and Decorative Image as well as a note in the margins that specifies what the aria-label should be. When using Preset annotations, there are fewer things to add to the canvas and the annotation process takes less time.

With Code Connect set up, Lukas added a hidden layer in the IconButton Figma component. It has a text property for aria-label which lets designers add the value directly from the component properties panel. No annotations needed. The hidden layer doesn’t disrupt any of the visuals, and the aria-label property gets exported directly with the rest of the component’s code.

An IconButton component with a code-review icon. On the left is a screenshot of the component’s properties panel, with an aria-label value of: Start code review. On the right is the Code Connect output showing usable React code for an IconButton that includes the parameter: aria-label=Start code review.

It takes time to set up Code Connect with each of your design system components. Here are a few tips to help:

  • Consistency is key. Make sure that the properties you create and how you place hidden layers is consistent across components. This helps set clear expectations so your teams can understand how these hidden layers and properties function. 
  • Use a branch of your design system library to experiment. Hiding attributes like aria-label is quite simple compared to other complex information that Preset annotations are capable of handling. 
  • Use visual regression testing (VRT). Adding complexity directly to a component comes with increased risk of things breaking in the future, especially for those with many variants. Figma’s merge conflict UI is helpful, but may not catch everything.

We’ve made the GitHub Annotation Toolkit open source, so you can see first-hand how we’ve implemented our Primer A11y Preset annotations and visual regression tests. Check it out and start annotating today!

Figma library cover for the GitHub Annotation Toolkit with a grid background that looks like a starry night sky. There's an armada of little annotation stamp labels covering the bottom two thirds of the image, all at an angle. There's a series of angled git lines above them. Both look like they're launching from the ground and through into the sky grid.

Further reading

Accessibility annotation kits are a great resource, provided they’re used responsibly. Eric Bailey, one of the contributors to our forthcoming GitHub Annotation Toolkit, has written extensively about how annotations can highlight and amplify deeply structural issues when you’re building digital products.

The post Design system annotations, part 2: Advanced methods of annotating components appeared first on The GitHub Blog.

Design system annotations, part 1: How accessibility gets left out of components

Post Syndicated from Jan Maarten original https://github.blog/engineering/user-experience/design-system-annotations-part-1-how-accessibility-gets-left-out-of-components/


When it comes to design systems, every organization tends to be at a different place in their accessibility journey. Some have put a great deal of work into making their design system accessible while others have a long way to go before getting there. To help on this journey, many organizations rely on accessibility annotations to make sure there are no access barriers when a design is ready to be built. 

However, it’s a common misconception (especially for organizations with mature design systems) that accessible components will result in accessible designs. While design systems are fantastic for scaling standards and consistency, they can’t prevent every issue with our designs or how we build them. Access barriers can still slip through the cracks and make it into production.

This is the root of the problem our Accessibility Design team set out to solve. 

In this two-part series, we’ll show you exactly how accessible design system components can produce inaccessible designs. Then we’ll demonstrate our solution: integrating annotations with our Primer components. This allows us to spend less time annotating, increases design system adoption, and reaches teams who may not have accessibility support. And in our next post, we’ll walk you through how you can do the same for your own components.

Let’s dig in.

What are annotations and their benefits? 

Annotations are notes included in design projects that help make the unseen explicit by conveying design intent that isn’t shown visually. They improve the usability of digital experiences by providing a holistic picture for developers of how an experience should function. Integrating annotations into our design process helps our teams work better together by closing communication gaps and preventing quality issues, accessibility audit issues, and expensive re-work. 

Some of the questions annotations help us answer include:

  • How is assistive technology meant to navigate a page from one element to another?
  • What’s the alternative text for informative images and buttons without labels?
  • How does content shift depending on viewport size, screen orientation, or zoom level?
  • Which virtual keyboard should be used for a form input on mobile?
  • How should focus be managed for complex interactions?

Our answers to questions like this—or the lack thereof—can make or break the experience of the web for a lot of people, especially users with disabilities. Some annotation tools are built specifically to help with this by guiding designers to include key details about web standards, platform functionality, and accessibility (a11y). 

Most public annotation kits are well suited for teams who are creating new design system components, teams who aren’t already using a design system, or teams who don’t have specialized accessibility knowledge. They usually help annotate things like:

  • Controls such as buttons and links
  • Structural elements such as headings and landmarks
  • Decorative images and informative descriptions 
  • Forms and other elements that require labels and semantic roles 
  • Focus order for assistive technology and keyboard navigation

GitHub’s annotation’s toolkit

One of our top priorities is to meet our colleagues where they’re at. We wanted all our designers to be able to use annotations out of the box because we believe they shouldn’t need to be a certified accessibility specialist in order to get things built in an accessible way. 

 A browser window showing the Web Accessibility Annotation Kit in the cvs-health/annotations repository.

To this end, last year we began creating an internal Figma library—the GitHub Annotation Toolkit—which is now open source! Our toolkit builds on the legacy of the former Inclusive Design team at CVS Health. Their two open source annotation kits help make documentation that’s easy to create and consume, and are among the most widely used annotation libraries in the Figma Community. 

While they add clarity, annotations can also add overhead. If teams are only relying on specialists to interpret designs and technical specifications for developers, the hand-off process can take longer than it needs to. To create our annotation toolkit, we rebuilt its predecessor from the ground up to avoid that overhead, making extensive improvements and adding inline documentation to make it more intuitive and helpful for all of our designers—not just accessibility specialists. 

Design systems can also help reduce that overhead. When you audit your design systems for accessibility, there’s less need for specialist attention on every product feature, since you’re using annotations to add technical semantics and specialist knowledge into every component. This means that designers and developers only need to adhere to the usage guidelines consistently, right?

The problems with annotations and design system components

Unfortunately, it’s not that simple. 

Accessibility is not binary

While design systems can help drive more accessible design at scale, they are constantly evolving and the work on them is never done. The accessibility of any component isn’t binary. Some may have a few severe issues that create access barriers, such as being inoperable with a keyboard or missing alt text. Others may have a few trivial issues, such as generic control labels. 

Most of the time, it will be a misnomer to claim that your design system is “fully accessible.” There’s always more work to do—it’s just a question of how much. The Web Content Accessibility Guidelines (WCAG) are a great starting point, but their “Success Criteria” isn’t tailored for the unique context that is your website or product or audience. 

While the WCAG should be used as a foundation to build from, it’s important to understand that it can’t capture every nuance of disabled users’ needs because your users’ needs are not every user’s needs. It would be very easy to believe that your design system is “fully accessible” if you never look past WCAG to talk to your users. If Primer has accessible components, it’s because we feel that direct participation and input from daily assistive technology users is the most important aspect of our work. Testing plans with real users—with and without disabilities—is where you really find what matters most. 

Accessible components do not guarantee accessible designs

Arranging a series of accessible components on a page does not automatically create an accurate and informative heading hierarchy. There’s a good chance that without additional documentation, the heading structure won’t make sense visually—nor as a medium for navigating with assistive technology.

A page wireframe showing a linear layout of an H1 title, an H2 in a banner below it, and a row of several cards below with headings of H4. The caption reads: this accessible card has an H4, breaking the page structure by skipping heading levels. Next to the wireframe is a diagram showing the page structure as a tree view, highlighting the level skipping from H2 to H4.

It’s great when accessible components are flexible and responsive, but what about when they’re placed in a layout that the component guidance doesn’t account for? Do they adapt to different zoom levels, viewport sizes, and screen orientations? Do they lose any functionality or context when any of those things change?

Component usage is contextual. You can add an image or icon to your design, but the design system docs can’t write descriptive text for you. You can use the same image in multiple places, but the image description may need to change depending on context. 

Similarly, forms built using the same input components may do different things and require different error validation messages. It’s no wonder that adopting design system components doesn’t get rid of all audit issues.

Design system components in Figma don’t include all the details

Annotation kits don’t include components for specific design systems because almost every organization is using their own. When annotation kits are adopted, teams often add ways to label their design system components. 

This labeling lets developers know they can use something that’s already been built, and that they don’t need to build something from scratch. It also helps identify any design system components that get ‘detached’ in Figma. And it reduces the number of things that need to be annotated. 

Let’s look at an example:

A green Primer button with a lightning bolt icon and a label that says: this button does something. To the right is a set of Figma component properties that control the button’s visual appearance.

If we’re using this Primer Button component from the Primer Web Figma library, there are a few important things that we won’t know just by looking at the design or the component properties:

  • Functional differences when components are implemented. Is this a link that just looks visually like a button? If so, a developer would use the <LinkButton> React component instead of <Button>.
  • Accessible labels for folks using assistive technology. The icon may need alt text. In some cases, the button text might need some visually-hidden text to differentiate it from similar buttons. How would we know what that text is? Without annotations, the Figma component doesn’t have a place to display this.
  • Whether user data is submitted. When a design doesn’t include an obvious form with input fields, how do we convey that the button needs specific attributes to submit data? 

It’s risky to leave questions like this unanswered, hoping someone notices and guesses the correct answer. 

A solution that streamlines the annotation process while minimizing risk

When creating new components, a set of detailed annotations can be a huge factor in how robust and accessible they are. Once the component is built, design teams can start to add instances of that component in their designs. When those designs are ready to be annotated, those new components shouldn’t need to be annotated again. In most cases, it would be redundant and unnecessary—but not in every case. 

There are some important details in many Primer components that may change from one instance to another. If we use the CVS Health annotation kit out of the box, we should be able to capture those variations, but we wouldn’t be able to avoid those redundant and unnecessary annotations. As we built our own annotation toolkit, we built a set of annotations for each Primer component to do both of those things at once. 

An annotated Primer Brand accordion with six Stamps and four Detail notes in the margins.

This accordion component has been thoroughly annotated so that an engineer has everything they need to build it the first time. These include heading levels, semantics for <detail> and <summary> elements, landmarks, and decorative icons. All of this is built into the component so we don’t need to annotate most of this when adding the accordion to our new designs.

However, there are two important things we need to annotate, as they can change from one instance to another:

  1. The optional title at the top.
  2. The heading level of each item within the accordion.

If we don’t specify these things, we’re leaving it to chance that the page’s heading structure will break or that the experience will be confusing for people to understand and navigate the page. The risks may be low for a single button or basic accordion, but they grow with pattern complexity, component nesting, interaction states, duplicated instances, and so on. 

An annotated Primer Brand accordion with one Stamp and one Detail note in the margins.

Instead of annotating what’s already built into the component or leaving these details to chance, we can add two quick annotations. One Stamp to point to the component, and one Details annotation where we fill in some blanks to make the heading levels clear. 

Because the prompts for specific component details are pre-set in the annotation, we call them Preset annotations.

A mosaic of preset annotation for various Primer components.

Introducing our Primer A11y Preset annotations

With this proof of concept, we selected ten frequently used Primer components for the same treatment and built a new set of Preset annotations to document these easily missed accessibility details—our Primer A11y Presets. 

Those Primer components tend to contribute to more accessibility audit issues when key details are missing on implementation. Issues for these components relate to things like lack of proper labels, error validation messages, or missing HTML or ARIA attributes

IconButton Preset annotation, with guidance toggled on.

Each of our Preset annotations is linked to component docs and Storybook demos. This will hopefully help developers get straight to the technical info they need without designers having to find and add links manually. We also included guidance for how to fill out each Preset, as well as how to use the component in an accessible way. This helps designers get support inline without leaving their Figma canvas. 

Want to create your own? Check out Design system annotations, part 2

Button components in Google’s Material Design and Shopify’s Polaris, IBM’s Carbon, or our Primer design system are all very different from one another. Because Preset annotations are based on specific components, they only work if you’re also using the design system they’re made for. 

In part 2 of this series, we’ll walk you through how you can build your own set of Preset annotations for your design system, as well as some different ways to document important accessibility details before development starts.

You may also like: 

If you’re more of a visual learner, you can watch Alexis Lucio explore Preset annotations during GitHub’s Dev Community Event to kick off Figma’s Config 2024. 

Get the guide to GitHub’s Annotation Toolkit >

The post Design system annotations, part 1: How accessibility gets left out of components appeared first on The GitHub Blog.

Accelerating GitHub theme creation with color tooling

Post Syndicated from Cole Bemis original https://github.blog/2022-06-14-accelerating-github-theme-creation-with-color-tooling/

Dark mode is no longer a nice-to-have feature. It’s an expectation. Yet, for many teams, implementing dark mode is still a daunting task.

Creating a palette for dark interfaces is not as simple as inverting colors and complexity increases if your team is planning multiple themes. Many people find themselves using a combination of disjointed color tools, which can be a painful experience.

GitHub dark mode (unveiled at GitHub Universe in December 2020) was the result of trial and error, copy and paste, as well as back and forth in a Figma file (with more than 370,000 layers!).

A screenshot of the Figma file we made while designing GitHub dark mode
A screenshot of the Figma file we made while designing GitHub dark mode

A few months after shipping dark mode, we began working on a dark high contrast theme to provide an option that maximizes legibility. While we were designing this new theme, we set out to improve our workflow by building an experimental tool to solve some of the challenges we encountered while designing the original dark color palette.

We’re calling our experimental color tool Primer Prism.

A sneak peek of Primer Prism
A sneak peek of Primer Prism

Part of GitHub’s Primer ecosystem, Primer Prism is a tool for creating and maintaining cohesive, consistent, and accessible color palettes. It allows us to:

  • Create or import color scales.
  • Adjust colors in a perceptually uniform color space (HSLuv).
  • Check contrast of color pairs.
  • Edit lightness curves across multiple color scales at once.
  • Export color palettes to production-ready code (JSON).

Our workflow

Our improved workflow for creating color palettes with Primer Prism is an iterative cycle comprised of three steps:

  1.  Defining tones
  2. Choosing colors
  3. Testing colors

Defining tones

We start by defining the color palette’s tonal character and contrast needs:

  • How light or dark should the background be?
  • What should the contrast ratio between the foreground and background be?

Although each palette will have a unique tonal character, we are mindful that all palettes meet contrast accessibility guidelines.

In Primer Prism, we start a new color palette by creating a new color scale and adjusting the lightness curve. In this phase, we’re only concerned with lightness and contrast. We’ll revisit hue and saturation later.

As we change the lightness of each color, Primer Prism checks the contrast of potential color pairings in the scale using the WCAG 2 standard.

Dragging lightness sliders up and down to adjust the lightness curve of a scale
Dragging lightness sliders up and down to adjust the lightness curve of a scale

Primer Prism also allows us to share curves across multiple color scales. So, when we have more scales, we can quickly change the tonal character of the entire color palette by adjusting a single lightness curve.

Adjusting the lightness curve of all color scales at once
Adjusting the lightness curve of all color scales at once

Primer Prism uses the HSLuv color space to ensure that the lightness values are perceptually uniform across the entire palette. In the HSLuv color space, two colors with the same lightness value will look equally bright.

Choosing colors

Next, we define the overall color character of our palette:

  • What hues do we need (for example: red, blue, green, etc.)?
  • How vibrant do we want the colors to be?

We create a color scale for every hue using the same lightness curve we made earlier. Then, we compare and adjust the base color (the fifth step in the scale) across all the color scales until the palette feels cohesive and consistent.

A side-by-side comparison of every color scale
A side-by-side comparison of every color scale

After deciding on the base color for each scale, we fine-tune the tints (lighter colors) and shades (darker colors). Blue, for example, shifts towards green hues in the tints and purple hues in the shades.

The hue, saturation, and lightness curves of the blue color scale
The hue, saturation, and lightness curves of the blue color scale

Fine-tuning color scales is more of an art than a science and often requires many micro-adjustments before the colors “feel right.” Check out Color in UI Design: A (Practical) Framework by Eric D. Kennedy to learn more about the fundamentals of designing color scales.

Testing colors

To test our colors in real-world scenarios, we export the palette from Primer Prism as a JSON object and add it to Primer Primitives, our repository of design tokens. We use pre-releases of the Primer Primitives package to test new color palettes on GitHub.com.

The dark color palette applied to GitHub.com
The dark color palette applied to GitHub.com

What’s next

We used Primer Prism to design several new color palettes, accelerating the creation of dark high contrast, light high contrast, and colorblind themes for GitHub. Next, we plan to improve our tooling to support the following key workflows.

Visual testing workflow

We plan to integrate visual testing directly into Primer Prism. Currently, visual testing of color palettes happens outside of Primer Prism, typically in Figma or production applications. However, we want a more convenient way to visualize how the colors will look when mapped to functional variables and used in actual user interfaces.

GitHub workflow

We plan to integrate GitHub into Primer Prism. Right now, it’s a hassle to edit existing color palettes because Primer Prism is not connected to the GitHub repository where we store color variables (Primer Primitives). A GitHub integration will allow us to directly pull from and push to the Primer Primitives repository.

Figma workflow

Our designers use Figma to explore and test new design ideas. We plan to create a Figma plugin to seamlessly integrate Primer Prism into their workflow.

Try it out

Primer Prism is open source and available for anyone to use at primer.style/prism.

We’d love to hear what you think. If you have feedback, please create an issue or start a discussion in the GitHub repository.

Warning: Primer Prism is experimental. Expect bugs and breaking changes as we continue to iterate.

Thanks

Huge shout-out to @Juliusschaeper, @auareyou, @edokoa, and @broccolini for their incredible work on the GitHub dark mode color palette.

Primer Prism was inspired by many existing color tools:
ColorBox by Lyft
Components AI
Huetone by Alexey Ardov
Leonardo by Adobe
Palettte by Gabriel Adorf
Palx by Brent Jackson
Scale by Hayk An

Further reading

Hawkins: Diving into the Reasoning Behind our Design System

Post Syndicated from Netflix Technology Blog original https://netflixtechblog.com/hawkins-diving-into-the-reasoning-behind-our-design-system-964a7357547

Stranger Things imagery showcasing the inspiration for the Hawkins Design System

by Hawkins team member Joshua Godi; with art contributions by Wiki Chaves

Hawkins may be the name of a fictional town in Indiana, most widely known as the backdrop for one of Netflix’s most popular TV series “Stranger Things,” but the name is so much more. Hawkins is the namesake that established the basis for a design system used across the Netflix Studio ecosystem.

Have you ever used a suite of applications that had an inconsistent user experience? It can be a nightmare to work efficiently. The learning curve can be immense for each and every application in the suite, as the user is essentially learning a new tool with each interaction. Aside from the burden on these users, the engineers responsible for building and maintaining these applications must keep reinventing the wheel, starting from scratch with toolsets, component libraries and design patterns. This investment is repetitive and costly. A design system, such as the one we developed for the Netflix Studio, can help alleviate most of these headaches.

We have been working on our own design system that is widely used across the Netflix Studio’s growing application catalogue, which consists of 80+ applications. These applications power the production of Netflix’s content, from pitch evaluation to financial forecasting and completed asset delivery. A typical day for a production employee could require using a handful of these applications to entertain our members across the world. We wanted a way to ensure that we can have a consistent user experience while also sharing as much code as possible.

In this blog post, we will highlight why we built Hawkins, as well as how we got buy-in across the engineering organization and our plans moving forward. We recently presented a talk on how we built Hawkins; so if you are interested in more details, check out the video.

What is a design system?

Before we can dive into the importance of having a design system, we have to define what a design system means. It can mean different things to different people. For Hawkins, our design system is composed of two main aspects.

General design system component mocks

First, we have the design elements that form the foundational layer of Hawkins. These consist of Figma components that are used throughout the design team. These components are used to build out mocks for the engineering team. Being the foundational layer, it is important that these assets are consistent and intuitive.

Second, we have our React component library, which is a JavaScript library for building user interfaces. The engineering team uses this component library to ensure that each and every component is reusable, conforms to the design assets and can be highly configurable for different situations. We also make sure that each component is composable and can be used in many different combinations. We made the decision to keep our components very atomic; this keeps them small, lightweight and easy to combine into larger components.

At Netflix, we have two teams composed of six people who work together to make Hawkins a success, but that doesn’t always need to be the case. A successful design system can be created with just a small team. The key aspects are that it is reusable, configurable and composable.

Why is a design system important?

Having a solid design system can help to alleviate many issues that come from maintaining so many different applications. A design system can bring cohesion across your suite of applications and drastically reduce the engineering burden for each application.

Examples of Figma components for the Hawkins Design System

Quality user experience can be hard to come by as your suite of applications grow. A design system should be there to help ease that burden, acting as the blueprint on how you build applications. Having a consistent user experience also reduces the training required. If users know how to fill out forms, access data in a table or receive notifications in one application, they will intuitively know how to in the next application.

The design system acts as a language that both designers and engineers can speak to align on how applications are built out. It also helps with onboarding new team members due to the documentation and examples outlined in your design system.

The last and arguably biggest win for design systems is the reduction of burden on engineering. There will only be one implementation of buttons, tables, forms, etc. This greatly reduces the number of bugs and improves the overall health and performance of every application that uses the design system. The entire engineering organization is working to improve one set of components vs. each using their own individual components. When a component is improved, whether through additional functionality or a bug fix, the benefit is shared across the entire organization.

Taking a wide view of the Netflix Studio landscape, we saw many opportunities where Hawkins could bring value to the engineering organization.

Build vs. buy

The first question we asked ourselves is whether we wanted to build out an entire design system from scratch or leverage an existing solution. There are pros and cons to each approach.

Building it yourself— The benefits of DIY means that you are in control every step of the way. You get to decide what will be included in the design system and what is better left out. The downside is that because you are responsible for it all, it will likely take longer to complete.

Leveraging an existing solution — When you leverage an existing solution, you can still customize certain elements of that solution, but ultimately you are getting a lot out of the box for free. Depending on which solution you choose, you could be inheriting a ton of issues or something that is battle tested. Do your research and don’t be afraid to ask around!

For Hawkins, we decided to take both approaches. On the design side, we decided to build it ourselves. This gave us complete creative control over how our user experience is throughout the design language. On the engineering side, we decided to build on top of an existing solution by utilizing Material-UI. Leveraging Material-UI, gave us a ton of components out of the box that we can configure and style to meet the needs of Hawkins. We also chose to obfuscate a number of the customizations that come from the library to ensure upgrading or replacing components will be smoother.

Generating users and getting buy-in

The single biggest question that we had when building out Hawkins is how to obtain buy-in across the engineering organization. We decided to track the number of uses of each component, the number of installs of the packages themselves, and how many applications were using Hawkins in production as metrics to determine success.

There is a definitive cost that comes with building out a design system no matter the route you take. The initial cost is very high, with research, building out the design tokens and the component library. Then, developers have to begin consuming the libraries inside of applications, either with full re-writes or feature by feature.

Graph depicting the cost of building a design system

A good representation of this is the graph above. While an organization may spend a lot of time initially making the design system, it will benefit greatly once it is fully implemented and trusted across the organization. With Hawkins, our initial build phase took about two quarters. The two quarters were split between Q1 consisting of creating the design language and Q2 being the implementation phase. Engineering and Design worked closely during the entire build phase. The end result was a significant number of components in Figma and a large component library leveraging Material-UI. Only then could we start to look for engineering teams to start using Hawkins.

When building out the component library, we set out to accomplish four key aspects that we felt would help drive support for Hawkins:

Document components — First, we ensured that each component was fully documented and had examples using Storybook.

On-call rotation for support — Next, we set up an on-call rotation in Slack, where engineers could not only seek guidance, but report any issues they may have encountered. It was extremely important to be responsive in our communication channels. The more support engineers feel they have, the more receptive they will be to using the design library.

Demonstrate Hawkins usefulness — Next, we started to do “road shows,” where we would join team meetings to demonstrate the value that Hawkins could bring to each and every team. This also provided an opportunity for the engineers to ask questions in person and for us to gather feedback to ensure our plans for Hawkins would meet their needs.

Bootstrap features for proof of concept— Finally, we helped bootstrap out features or applications for teams as a proof of concept. All of these together helped to foster a relationship between the Hawkins team and engineering teams.

Even today, as the Hawkins team, we run through all of the above exercises and more to ensure that the design system is robust and has the level of support the engineering organization can trust.

Handling the outliers

The Hawkins libraries all consist of basic components that are the building blocks to the applications across the Netflix Studio. When engineers increased their usage of Hawkins, it became clear that many folks were using the atomic components to build more complex experiences that were common across multiple applications, like in-app chat, data grids, and file uploaders, to name a few. We did not want to put these components straight into Hawkins because of the complexity and because they weren’t used across the entire Studio. So, we were tasked with identifying a way to share these complex components while still being able to benefit from all the work we accomplished on Hawkins.

To meet this challenge, developers decided to spin up a parallel library that sits right next to Hawkins. This library builds on top of the existing design system to provide a home for all the complex components that didn’t fit into the original design system.

Venn diagram showing the relationship between the libraries

This library was set up as a Lerna monorepo with tooling to quickly jumpstart a new package. We followed the same steps as Hawkins with Storybook and communication channels. The benefit of using a monorepo was that it gave engineering a single place to discover what components are available when building out applications. We also decided to version each package independently, which helped avoid issues with updating Hawkins or in downstream applications.

With so many components that will go into this parallel library, we decided on taking an “open source” approach to share the burden of responsibility for each component. Every engineer is welcome to contribute new components and help fix bugs or release new features in existing components. This model helps spread the ownership out from just a single engineer to a team of developers and engineers working in tandem.

It is the goal that eventually these components could be migrated into the Hawkins library. That is why we took the time to ensure that each repository has the same rules when it came to development, testing and building. This would allow for an easy migration.

Wrapping up

We still have a long way to go on Hawkins. There are still a plethora of improvements that we can do to enhance performance and developer ergonomics, and make it easier to work with Hawkins in general, especially as we start to use Hawkins outside of just the Netflix Studio!

Logo for the Hawkins Design System

We are very excited to share our work on Hawkins and dive into some of the nuances that we came across.


Hawkins: Diving into the Reasoning Behind our Design System was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.