Automated Cross-Browser Testing for WebGL— It’s Not Going to Happen

Apologies to the folks who found this post while searching for “automated WebGL testing,” “how to write cross-browser WebGL tests,” or similar. I’ve been there, and it is not my favorite part of the job. Sadly I do not know a magic recipe for writing cross-browser acceptance tests for web apps that integrate WebGL canvas interactions as part of a larger user flow. This post offers a look into how the Reserved squad at Eventbrite uses Rainforest QA to test complex WebGL flows.

I’m a frontend software engineer on the Reserved squad, which recently (at the time of writing) launched an end-to-end experience for reserving seats within Eventbrite’s embedded checkout flow. While we were developing this feature, we ran into a roadblock: how could we write reliable acceptance tests for our WebGL-dependent flows? Furthermore, how could we reliably test our user flows without sinking hundreds of additional engineering hours into coercing Selenium to click on the precise canvas coordinates necessary to reserve a seat? We decided to try testing some of our user flows with a crowdsourced quality assurance (QA) platform called Rainforest QA, and have been quite happy to ship the results.

WebGL: What it’s good at, and one unfortunate consequence

WebGL is useful for rendering complex 2D and 3D graphics in the client’s web browser. It’s natively supported by all major browsers and under the hood interfaces with OpenGL API to render content in the canvas element. Because it allows code to run in the client’s GPU, there are significant performance benefits when you need to render and listen to actions on hundreds or thousands of elements.

My squad at Eventbrite uses WebGL (with help from Three.js, which you can learn more about in an earlier blog post) to render customizable venue maps that allow organizers to determine seat selling order. Once the organizer publishes the event, we allow attendees to choose the location of their seat on the rendered venue map. Because WebGL draws the venue maps in the canvas element rather than needlessly generating DOM elements for every seat, we can provide a relatively performant experience, even for maps with tens of thousands of seats. The only major drawback is that there is no DOM element to target in our acceptance tests when we want to test what happens when a user clicks on a seat.

The code to render a seat map using Three.js looks roughly like this:

// Initialize scene, camera values based on client browser width
const {scene, camera} = getSceneAndCamera();
const element = document.getElementById('canvas');
const renderer = new THREE.WebGLRenderer();

// Add objects like seats, stage, etc. to the scene, then render it
addObjectsToScene(scene);
renderer.render(scene, camera);

This code renders content in the canvas element:

But when we inspect the generated markup, this is all that we see:

<canvas width="719" height="656"></canvas>

Because the canvas element does not contain targetable DOM elements, simulating a seat click using WebDriver or other test scripting frameworks requires specifying exact coordinates within the canvas element.

How did Rainforest solve our testing problem?

For several months, my squad had been working in a green pasture of unreleased code as we made steady progress on new pick-a-seat features. Throughout the development process, we maintained test coverage with unit tests, integration tests, and page-level JS functional tests using enzyme and fetch-mock. However, our test coverage contained a glaring hole: we had not yet written tests that fully verified our user stories.

Acceptance tests are black-box tests that formally describe a user story and that we run at the system level. An acceptance test script might load a URL in a virtual machine (VM), automate some user actions, and confirm that the user can complete a flow (such as checkout) as expected. Eventbrite engineers rely on acceptance tests to ensure that our user interfaces don’t break when squads across the organization push code to our shared, continuously deployed repositories. Most acceptance tests at Eventbrite are written using Selenium WebDriver and often look something like this:

    def test_checkout_widget_free_event(self):
        """Verify it is possible to purchase a free ticket."""
        
        # Go to the test page
        self.checkout_widget.go_to_widget_test_page()

        # Select a ticket and click the checkout button
        self.checkout_widget.select_ticket_quantity(free_ticket.id, 1)
        self.checkout_widget.click_checkout_button()

        # Verify the purchase confirmation page is displayed
        self.checkout_widget.verify_purchase_confirmation_page_rendered()

But when targeting a canvas element, clicking on a seat looks a bit more like this:

   action = ActionChains(webdriver_instance)
   action.move_by_offset(seat_px, seat_py)
   action.click()
   action.perform()

In other words, we need to know the exact x and y coordinates of the seat within the canvas element. Even after the chore of automating clicks on precise coordinates within the canvas, we knew that minor style changes might require us to revisit each test and hunt down updated coordinates.

As the projected release date loomed near, we considered our options and determined that it would require several dedicated sprints to write the tests needed to thoroughly cover all of our new features. What if, instead of wrangling data and coordinates, we could write out test plans that could be quickly verified by human QA testers?

Enter Rainforest! Rainforest is a crowdsourced QA solution that puts our flow in front of real users. Because testers access sessions through a VM, we can specify which browsers they need to test, and they can run the tests against our staging environment. The Rainforest app runs the test suite on a customizable schedule, and the entire test run is parallelized and completed in less than 30 minutes. We wrote out all of our as-yet-untested user story test cases (in plain English) and got the system up and running.

Our Rainforest tests look like this:

We write each step of the test as a direction, followed by a yes-or-no question for the tester to answer. During a testing session, the tester follows the instructions, such as: “Click ‘Buy on Map’ located on the right-hand side.” Next, they mark the step as passed if the click caused the rendered map to zoom to the two highlighted seats.

Our key to Rainforest success: one-step event creation

Once we decided to proceed with this approach, our squad invested some time into developing an API that would allow us to automate a critical step of this workflow. When Rainforest testers log into their VMs, we provide them a URL that will, upon load, create a new QA user account with an event that is in the exact state needed to test the features covered by the test. A tester loading this URL is analogous to an acceptance test run instantiating the factory classes that generate test data for our WebDriver tests.

The endpoint accepts URL parameters that define relevant features of the event:

/testing/create_event/?redirect=checkout&map_size=medium&num_ticket_types=4

Loading this URL creates a new QA user with restricted permissions, builds an event with a medium-sized seat map and four ticket types (authored by the new user), and then redirects to the embedded checkout test URL for the given event.

Without this tool, Rainforest testing would require a manual tester dozens of clicks and page refreshes to create an event, design a venue map, publish the event, and then finally reach the checkout flow. Eventbrite engineers have already covered all of these actions with automated acceptance tests elsewhere—when we are testing the seat reservation flow, we want to focus on precisely that. One-step event creation has allowed us to get testers into the correct state to access our flow with a single keystroke.

Additionally, because we have configured Rainforest to run against our staging environment, Rainforest QA testers catch bugs for us before they are released. While unit and integration tests give us confidence that our code works at a more granular level, Rainforest has given us an additional layer of security, assuring that the features we already built are still working so that we can move on to the next challenge.

Universal takeaways

Yes, Rainforest does cost money, and I’m not here to tell you how your company should spend its money. (If you’re curious about Rainforest, you can always request a demo). It’s also not the only solution in this space. Rainforest works very well for us, but a related platform such as Testlio, GlobalAppTesting, TestingBot, or UseTrace may be a better fit for your team.

Here are some takeaway learnings from our case study that might still come in handy:

  • Cross-browser testing pays off. If your current acceptance suite only runs tests against one browser, it might be worth re-evaluating. (If you’re doing your own cross-browser QA, Browserstack is indispensable.)
  • When you automate testing user stories as part of your continuous integration (CI) flow, you ensure that your system reliably meets product requirements.
  • Don’t stop writing automated tests, but do consider how much time you are spending writing and maintaining tests that could be more reliably tested by a human QA tester.
  • You can get the most out of your testing and QA by automating critical steps of the process.

For my squad, Rainforest has been an excellent solution and has helped us catch many browser-specific and complex multi-page bugs before they made their way to the release branch. While we are still working on improving its visibility in our CI flow so that newly introduced bugs are surfaced earlier in the development cycle, automated test runs assure us that our features remain stable across all major browsers. As a developer, I love that I get to spend my time building new features rather than writing and maintaining fussy WebDriver tests.

Have you found another way to save time writing acceptance tests for complex WebGL flows? Do you have questions about our Rainforest experience that I didn’t cover? Do you want to have a conversation about the ethics of crowdsourcing QA work? Let me know what you think in the comments below or on Twitter.

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