I’m developing a pytest project to test an API. How can I pass environment information into my tests? I need to run tests against different environments like DEV, TEST, and PROD. Each environment has a different URL and a unique set of users.
This is a common problem for automated test suites, not just in Python or pytest. Any information a test needs about the environment under test is called configuration metadata. URLs and user accounts are common configuration metadata values. Tests need to know what site to hit and how to authenticate.
Using config files with an environment variable
There are many ways to handle inputs like this. I like to create JSON files to store the configuration metadata for each environment. So, something like this:
Selecting the config file with a command line argument
If you don’t want to use environment variables to select the config file, you could instead create a custom pytest command line argument. Bas Dijkstra wrote an excellent article showing how to do this. Basically, you could add the following function to conftest.py to add the custom argument:
help='Path to the target environment config file')
Then, update the target_env fixture:
config_path = request.config.getoption('--target-env')
with open(config_path) as config_file:
config_data = json.load(config_file)
When running your tests, you would specify the config file path like this:
python -m pytest --target-env dev.json
Why bother with JSON files?
In theory, you could pass all inputs into your tests with pytest command line arguments or environment variables. You don’t need config files. However, I find that storing configuration metadata in files is much more convenient than setting a bunch of inputs each time I need to run my tests. In our example above, passing one value for the config file path is much easier than passing three different values for base URL, username, and password. Real-world test projects might need more inputs. Plus, configurations don’t change frequency, so it’s okay to save them in a file for repeated use. Just make sure to keep your config files safe if they have any secrets.
Whenever reading inputs, it’s good practice to make sure their values are good. Otherwise, tests could crash! I like to add a few basic assertions as safety checks:
config_path = request.config.getoption('--target-env')
with open(config_path) as config_file:
config_data = json.load(config_file)
assert 'base_url' in config_data
assert 'username' in config_data
assert 'password' in config_data
Now, pytest will stop immediately if inputs are wrong.
I started Boa Constrictor back in 2018 because I loathed page objects. On a previous project, I saw page objects balloon to several thousand lines long with duplicative methods. Developing new tests became a nightmare, and about 10% of tests failed daily because they didn’t handle waiting properly.
So, while preparing a test strategy at a new company, I invested time in learning the Screenplay Pattern. To be honest, the pattern seemed a bit confusing at first, but I was willing to try anything other than page objects again. Eventually, it clicked for me: Actors use Abilities to perform Interactions. Boom! It was a clean separation of concerns.
I also wanted to provide a standalone implementation of the Screenplay Pattern. Since the Screenplay Pattern is a design for automating interactions, it could and should integrate with any .NET test framework: SpecFlow, MsTest, NUnit, xUnit.net, and any others. With Boa Constrictor, we focused singularly on making interactions as excellent as possible, and we let other projects handle separate concerns. I did not want Boa Constrictor to be locked into any particular tool or system. In this sense, Boa Constrictor diverged from Serenity BDD – it was not meant to be a .NET version of Serenity, despite taking much inspiration from Serenity.
Furthermore, in the design and all the messaging for Boa Constrictor, I strived to make the Screenplay Pattern easy to understand. So many folks I knew gave up on Screenplay in the past because they thought it was too complicated. I wanted to break things down so that any automation developer could pick it up quickly. Hence, I formed the soundbite, “Actors use Abilities to perform Interactions,” to describe the pattern in one line. I also coined the project’s slogan, “Better Interactions for Better Automation,” to clearly communicate why Screenplay should be used over alternatives like raw calls or page objects.
So far, Boa Constrictor has succeeded modestly well in these goals. Now, the project is pursuing one more goal: democratizing the Screenplay Pattern.
At its heart, the Screenplay Pattern is a generic pattern for any kind of interactions. The core pattern should not favor any particular tool or package. Anyone should be able to implement interaction libraries using the tools (or “Abilities”) they want, and each of those libraries should be treated equally without preference. Recently, in our plans for Boa Constrictor 3, we announced that we want to create separate packages for the “core” pattern and for each library of interactions. We also announced plans to add new libraries for Playwright and Applitools. The existing libraries – Selenium WebDriver and RestSharp – need not be the only libraries. Boa Constrictor was never meant to be merely a WebDriver wrapper or a superior page object. It was meant to provide better interactions for any kind of test automation.
In version 3.0.0, we successfully separated the Boa.Constrictor project into three new .NET projects and released a NuGet package for each:
This separation enables folks to pick the parts they need. If they only need Selenium WebDriver interactions, then they can use just the Boa.Constrictor.Selenium package. If they want to implement their own interactions and don’t need Selenium or RestSharp, then they can use the Boa.Constrictor.Screenplay package without being forced to take on those extra dependencies.
Furthermore, we continued to maintain the “classic” Boa.Constrictor package. Now, this package simply claims dependencies on the other three packages in order to preserve backwards compatibility for folks who used previous version of Boa Constrictor. As part of the upgrade from 2.0.x to 3.0.x, we did change some namespaces (which are documented in the project changelog), but the rest of the code remained the same. We wanted the upgrade to be as straightforward as possible.
The core contributors and I will continue to implement our plans for Boa Constrictor 3 over the coming weeks. There’s a lot to do, and we will do our best to implement new code with thoughtfulness and quality. We will also strive to keep everything documented. Please be patient with us as development progresses. We also welcome your contributions, ideas, and feedback. Let’s make Boa Constrictor excellent together.
Boa Constrictor is the .NET Screenplay Pattern. It helps you make better interactions for better test automation!
I originally created Boa Constrictor starting in 2018 as the cornerstone of PrecisionLender‘s end-to-end test automation project. In October 2020, my team and I released it as an open source project hosted on GitHub. Since then, the Boa Constrictor NuGet package has been downloaded over 44K times, and my team and I have shared the project through multiple conference talks and webinars. It’s awesome to see the project really take off!
Unfortunately, Boa Constrictor has had very little development over the past year. The latest release was version 2.0.0 in November 2021. What happened? Well, first, I left Q2 (the company that acquired PrecisionLender) to join Applitools, so I personally was not working on Boa Constrictor as part of my day job. Second, Boa Constrictor didn’t need much development. The core Screenplay Pattern was well-established, and the interactions for Selenium WebDriver and RestSharp were battle-hardened. Even though we made no new releases for a year, the project remained alive and well. The team at Q2 still uses Boa Constrictor as part of thousands of test iterations per day!
The time has now come for new development. Today, I’m excited to announce our plans for the next phase of Boa Constrictor! In this article, I’ll share the vision that the core contributors and I have for the project – tentatively casting it as “version 3.” We will also share a rough timeline for development.
Separate interaction packages
Currently, the Boa.Constrictor NuGet package has three main parts:
The Screenplay Pattern’s core interfaces and classes
This structure is convenient for a test automation project that uses Selenium and RestSharp, but it forces projects that don’t use them to take on their dependencies. What if a project uses Playwright instead of Selenium, or RestAssured.NET instead of RestSharp? What if a project wants to make different kinds of interactions, like mobile interactions with Appium?
At its heart, the Screenplay Pattern is a generic pattern for any kind of interactions. In theory, the core pattern should not favor any particular tool or package. Anyone should be able to implement interaction libraries using the core pattern.
With that in mind, we intend to split the current Boa.Constrictor package into three separate packages, one for each of the existing parts. That way, a project can declare dependencies only on the parts of Boa Constrictor that it needs. It also enables us (and others) to develop new packages for different kinds of interactions.
One of the new interaction packages we intend to create is a library for Playwright interactions.Playwright is a fantastic new web testing framework from Microsoft. It provides several advantages over Selenium WebDriver, such as faster execution, automatic waiting, and trace logging.
We want to give people the ability to choose between Selenium WebDriver or Playwright for their web UI interactions. Since a test automation project would use only one, and since there could be overlap in the names and types of interactions, separating interaction packages as detailed in the previous section will be a prerequisite for developing Playwright support.
We may also try to develop an adapter for Playwright interactions that uses the same interfaces as Selenium interactions so that folks could switch from Selenium to Playwright without rewriting their interactions.
Another new interaction package we intend to create is a library for Applitools interactions.Applitools is the premier visual testing platform. Visual testing catches UI bugs that are difficult to catch with traditional assertions, such as missing elements, broken styling, and overlapping text. A Boa Constrictor package for Applitools interactions would make it easier to capture visual snapshots together with Selenium WebDriver interactions. It would also be an “optional” feature since it would be its own package.
Shadow DOM support
Shadow DOM is a technique for encapsulating parts of a web page. It enables a hidden DOM tree to be attached to an element in the “regular” DOM tree so that different parts between the two DOMs do not clash. Shadow DOM usage has become quite prevalent in web apps these days.
We intend to add support for Selenium interactions to pierce the shadow DOM. Selenium WebDriver requires extra calls to pierce the shadow DOM. Unfortunately, Boa Constrictor’s Selenium interactions currently do not support shadow DOM interactivity. Most likely, we will add new builder methods for Selenium-based Tasks and Questions that take in a locator for the shadow root element and then update the action methods to handle the shadow DOM if necessary.
.NET 7 targets
The main Boa Constrictor project, the unit tests project, and the example project all target .NET 5. Unfortunately, NET 5 is no longer supported by Microsoft. The latest release is .NET 7.
We intend to add .NET 7 targets. We will make the library packages target .NET 7, .NET 5 (for backwards compatibility), and .NET Standard 2.0 (again, for backwards compatibility). We will change the unit test and example projects to target .NET 7 exclusively. In fact, we have already made this change in version 2.0.2!
Many of Boa Constrictor’s dependencies have released new versions over the past year. GitHub’s Dependabot has also flagged some security vulnerabilities. It’s time to update dependency versions. This is standard periodic maintenance for any project. Already, we have updated our Selenium WebDriver dependencies to version 4.6.
Boa Constrictor has a doc site hosted using GitHub Pages. As we make the changes described above, we must also update the documentation for the project. Most notably, we will need to update our tutorial and example project, since the packages will be different, and we will have support for more kinds of interactions.
What’s the timeline?
The core contributors and I plan to implement these enhancements within the next three months:
Today, we just released two new versions with incremental changes: 2.0.1 and 2.0.2.
This week, we hope to split the existing package into three, which we intend to release as version 3.0.
It is one of the most recognizable works of art in the world. It is so famous, it has an emoji: 🌊.
The Great Wave Off Kanagawa is a Japanese woodblock print. It is not a painting or a drawing but a print. In Japanese, the term for this type of art is ukiyo-e, which means “pictures of the floating world.” Ukiyo-e prints first appeared around the 1660s and did not decline in popularity until the Meiji Restoration two centuries later. While most artists focused on subjects of people, late masters like Hokusai captured perspectives of landscapes and nature. Here, in The Great Wave, we see a giant wave, full of energy and ferocity, crashing down onto three fast boats attempting to transport live fish to market. Its vibrant blue water and stark white peaks contrast against a yellowish-gray sky. In the distance is Mount Fuji, the highest mountain in Japan, yet it is dwarfed in perspective by the waves. In fact, the water spray from the waves appears to fall over Mount Fuji like snow. If you didn’t look closely, you might presume that Mount Fuji is just the crest of another wave.
The Great Wave is absolutely stunning. It is arguably Hokusai’s finest work. The colors and the lines reflect boldness. The claws of the wave impart vitality. The men on the boat show submission and possibly fear. The spray from the wave reveals delicacy and attention to detail. Personally, I love ukiyo-e prints like this. I travel the world to see them in person. The quality, creativity, and craftsmanship they exhibit inspire me to instill the highest quality possible into my own work.
As software quality professionals, there are several lessons we can learn from ukiyo-e masters like Hokusai. Testing is an art as much as it is engineering. We can take cues from these prolific artists in how we approach quality in our own work. In this article, I will share how we can make our own “Great Waves” using 8 software testing convictions inspired by ukiyo-e prints like The Great Wave. Let’s begin!
Conviction #1: Focus on behavior
Although we hold these Japanese woodblock prints today in high regard, they were seen as anything but fancy centuries ago in Japan. Ukiyo-e was “low” art for the common people, whereas paintings on silk scrolls were considered “high” art for the high classes.
Folks would buy these prints from local merchants for slightly more than the cost of a bowl of noodles – about $5 to $10 US dollars today – and they would use these prints to decorate their homes. By comparison, a print of The Great Wavesold at auction for $1.11 million in September 2020.
These prints weren’t very large, either. The Great Wave measures 10 inches tall by 15 inches wide, and most prints were of similar size. That made them convenient to buy at the market, carry them home, and display on the wall. To understand how the Japanese people treated these prints in their day, think about the decorations in your homes that you bought at stores like Home Goods and Target. You probably have some screen prints or posters on your walls.
Since the target consumer for ukiyo-e prints were ordinary people with working-class budgets, they needed to be affordable, popular, and recognizable. When Hokusai published The Great Wave, it wasn’t a standalone piece. It was the first print in a series named Thirty-six Views of Mount Fuji. Below are three other prints from that series. The central feature in each print is Mount Fuji, which would be instantly recognizable to any Japanese person. The various views would also be relatable.
The features of these prints made them valuable. Anyone could find a favorite print or two out of a series of 36. They made art accessible. They were inexpensive yet impressive. They were artsy yet accessible. Artists like Hokusai knew what people wanted, and they delivered the goods.
This isn’t any different from software development. Features add value for the users. For example, if you’re developing a banking app, folks better be able to log in securely and view their latest transactions. If those features are broken or unintuitive, folks might as well move their accounts to other banks! We, as the developers and testers, are like the ukiyo-e artists: we need to know what our customers need. We need to make products that they not only want, but they also enjoy.
Features add value. However, I would use a better word to describe this aspect of a product: behavior. Behavior is the way one acts or conducts oneself. In software, we define behaviors in terms of inputs and responses. For example, login is a behavior: you enter valid credentials, and you expect to gain access. You gave inputs, the app did something, and you got the result.
My conviction on software testing AND development is that if you focus on good software behaviors, then everything else falls into place. When you plan development work, you prioritize the most important behaviors. When you test the features, you cover the most important behaviors. When users get your new product, they gain value from those features, and hopefully you make that money, just like Hokusai did.
This is why I strongly believe in the value of Behavior-Driven Development, or BDD for short. As a set of pragmatic practices, BDD helps you and your team stay focused on the things that matter. BDD involves activities like Three Amigos collaboration, Example Mapping, and writing Gherkin. When you focus on behavior – not on shiny new tech, or story points, or some other distractions – you win big.
Conviction #2: Prioritize on risk
Ukiyo-e artists depicted more than just views of Mount Fuji. In fact, landscape scenes became popular only during the late period of woodblock printing – the 1830s to the 1860s. Before then, artists focused primarily on people: geisha, courtesans, sumo wrestlers, kabuki actors, and legendary figures. These were all characters from the “floating world,” a world of pleasure and hedonism apart from the dreary everyday life of feudal Japan.
Here is a renowned print of a kabuki actor by Sharaku, printed in 1794:
Sharaku was active only for one year, but he produced some of the most expressive portraits seen during ukiyo-e’s peak period. A yakko was a samurai’s henchman. In this portrait, we see Edobei ready for dirty deeds, with a stark grimace on his face and hands pulsing with anger.
Why would artists like Sharaku print faces like these? Because they would sell. Remember, ukiyo-e was not high-class art. It was a business. Artists would make a series of prints and sell them on the streets of Edo (now Tokyo). They needed to make prints that people wanted to buy. If they picked lousy or boring subjects, their prints wouldn’t sell. No soba noodles for them! So, what subjects did they choose? Celebrities. Actors. “Female beauties.” And some content that was not safe for work, like Hokusai’s The Dream of the Fisherman’s Wife. (Seriously, that link is not safe for work. Click it at your own risk.)
Artists prioritized their work based on business risk. They chose subjects that would be easy to sell. They pursued value. As testers, we should also prioritize test coverage based on risk.
I know there’s a popular slogan saying, “Test all the things!”, but that’s just impossible. It’s like saying, “Print all the pictures!” Modern apps are too complex to attempt any sort of “complete” or “100%” coverage. Instead, we should focus our testing efforts on the most important behaviors, the ones that would cause the most problems if they broke. Testing is ultimately a risk-mitigating activity. We do testing to de-risk problems that enter during development.
So, what does a risk-based testing strategy look like? Well, start by covering the most valuable behaviors. You can call them the MVBs. These are behaviors that are core to your app. If they break, then it’s game over. No soba noodles. For example, if you can’t log in, you’re done-zo. The MVBs should be tested before every release. They are non-negotiable test coverage. If your team doesn’t have enough resources to run these tests, then get more resources.
In addition to the MVBs, cover areas that were changed since the previous release. For example, if your banking app just added mobile deposits, then you should test mobile deposits. Things break where developers make changes. Also, look at testing different layers and aspects of the product. Not every test should be a web UI test. Add unit tests to pinpoint failures in the code. Add API tests to catch problems at the service layer. Consider aspects like security, accessibility, and visuals.
When planning these tests, try to keep them fast and atomic, covering individual behaviors instead of long workflows. Shorter tests are more reliable and give space for more coverage. And if you do have the resources for more coverage beyond the MVBs and areas of change, expand your coverage as resources permit. Keep adding coverage for the next most valuable behaviors until you either run out of time or the coverage isn’t worth the time.
Overall, ask yourself this when weighing risks: How painful would it be if a particular behavior failed? Would it ruin a user’s experience, or would they barely notice?
Conviction #3: Automate
The copy of The Great Wave shown at the top of this article is located at the Metropolitan Museum of Art in New York City. However, that’s not the only version. When ukiyo-e artists produced their prints, they kept printing copies until the woodblocks wore out! Remember, these weren’t precious paintings for the rich, they were posters for the commoners. One set of woodblocks could print thousands of impressions of popular designs for the masses. It’s estimated that there were five to eight thousand original impressions of The Great Wave, but nobody knows for sure. To this day, only a few hundred have survived. And much to my own frustration, museums that have copies do not put them on public display because the pieces are so fragile.
Here are different copies of The Great Wave from different museums:
Print production had to be efficient and smooth. Remember, this was a business. Publishers would make more money if they could print more impressions from the same set of woodblocks. They’d gain more renown if their prints maintained high quality throughout the lifetime of the blocks. And the faster they could get their prints to market, the sooner they could get paid and enjoy all the soba noodles.
What can we learn from this? Automate! That’s our third conviction.
What can we learn from this? Automate! Automation is a force multiplier. If Hokusai spent all his time manually laboring over one copy of The Great Wave, then we probably wouldn’t be talking about it today. But because woodblock printing was a whole process, he produced thousands of copies for everyone to enjoy. I wouldn’t call the woodblock printing process fully “automated” because it had several tedious steps with manual labor, but in Edo period Japan, it was about as automated as you could get.
Compare this to testing. If we run a test manually, we cover the target behavior one time. That’s it: lots of labor for one instance. However, if we automate that test, we can run it thousands of times. It can deliver value again and again. That’s the difference between a painting and a print.
So, how should we go about test automation? First, you should define your goals. What do you hope to achieve with automation? Do you want to speed up your testing cycles? Are you looking to widen your test coverage? Perhaps you want to empower Continuous Delivery through Continuous Testing? Carefully defining your goals from the start will help you make good decisions in your test automation strategy.
When you start automating tests, treat it like full software development. You aren’t just writing a bunch of scripts, you are developing a software system. Follow recommended practices. Use design patterns. Do code reviews. Fix bugs quickly. These principles apply whether you are using coded or codeless tools.
Another trap to avoid is delaying test automation. So many times, I’ve heard teams struggle to automate their tests because they schedule automation work as their lowest priority. They wish they could develop automation, but they just never have the time. Instead, they grind through testing their MVBs manually just to get the job done. My advice is flip that attitude right-side up. Automate first, not last. Instead of planning a few tests to automate if there’s time, plan to automate first and cover anything that couldn’t be automated with manual testing.
Furthermore, integrate automated tests into the team’s Continuous Integration system as soon as possible. Automated tests that aren’t running are dead to me. Get them running automatically in CI so they can deliver value. Running them nightly or even weekly can be a good start, as long as they run on a continuous cadence.
Finally, learn good practices. Test automation technologies are ever-evolving. It seems like new tools and frameworks hit the market all the time. If you’re new to automation or you want to catch up with the latest trends, then take time to learn. One of the best resources I can recommend is Test Automation University. TAU has about 70 courses on everything you can imagine, taught by the best instructors in the world, and it’s 100% FREE!
Now, you might be thinking, “Andy, come on, you know everything can’t be automated!” And that’s true. There are times when human intervention adds value. We see this in ukiyo-e prints, too. Here is Plum Garden at Kameido by Utagawa Hiroshige, Hokusai’s main rival. Notice the gradient colors of green and red in the background:
Printers added these gradients using a technique called bokashi, in which they would apply layers of ink to the woodblocks by hand. Sometimes, they would even paint layers directly on the prints. In these cases, the “automation” of the printing process was insufficient, and humans needed to manually intervene.
It’s always good to have humans test-drive software. Automation is great for functional verification, but it can’t validate user experience. Exploratory testing is an awesome complement to automated testing because it mitigates different risks.
If we use Visual AI to compare these two prints, it will quickly identify the main difference:
The signature block is in a different location! Small differences like small pixel offsets are ignored, while major differences are highlighted. If you apply this style of visual testing to your web and mobile apps, you could catch a ton of visual bugs before they cause problems for your users. Modern test automation can do some really cool tricks!
Conviction #4: Shift left and right
Mokuhanga, or woodblock printing, was a huge process with multiple steps. Artists like Hokusai and Hiroshige did not print their artwork themselves. In fact, printing required multiple roles to be successful: a publisher, an artist, a carver, and a printer.
The publisher essentially ran the process. They commissioned, financed, and distributed prints. They would even collaborate with artists on print design to keep them up with the latest trends.
The artist designed the patterns for the prints. They would sketch the patterns on washi paper and give instructions to the carver and printer on how to properly produce the prints.
The carver would chisel the artist’s pattern into a set of wooden printing blocks. Each layer of ink would have its own block. Carvers typically used a smooth, hard wood like cherry.
The printer used the artist’s patterns and carver’s woodblocks to actually make the prints. They would coat the blocks in appropriately-colored water-based inks and then press paper onto the blocks.
Quality had to be considered at every step in the process, not just at the end. If the artist was not clear about colors, then the printer might make a mistake. If the carver cut a groove too deep, then ink might not adhere to the paper as intended. If the printer misaligned a page during printing, then they’d need to throw it away – wasting time, supplies, and woodblock life – or risk tarnishing everyone’s reputation with a misprint. Hokusai was noted for his stringent quality standards for carvers and printers.
This is just like software development. We can substitute the word “testing” for “inspection” in Deming’s quote. Testers don’t exclusively “own” quality. Every role – business, development, and testing – has a responsibility for high-caliber work. If a product owner doesn’t understand what the customer needs, or a developer skips code reviews, or if a tester neglects an important feature, then software quality will suffer.
How do we engage the whole team in quality work? Shift left and right.
Most testers are probably familiar with the term shift left. It means, start doing testing work earlier in the development process. Don’t wait until developers are “done” and throw their code “over the fence” to be tested. Run tests continuously during development. Automate tests in-sprint. Adopt test-driven and behavior-driven practices. Require unit tests. Add test implementation to the “Definition of Done.”
But what about shift right? This is a newer phase, but not necessarily a newer practice. Shift right means, continue to monitor software quality during and after releases. Build observability into apps. Monitor apps for bugs, failures, and poor performance. Do canary deployments to see how systems respond to updates. Perform chaos testing to see how resilient environments are to outages. Issue different UIs to user groups as part of A/B testing to find out what’s most effective. And feed everything you learn back into development a la “shift left.”
The famous DevOps infinity loop shows how “shift left” and “shift right” are really all part of the same flow. If you start in the middle where the paths cross, you can see arrows pointing leftward for feedback, planning, and building. Then, they push rightward with continuous integration, deployment, monitoring, and operations. We can (and should) take all the quality measures we said before as we spin through this loop perpetually. When we plan, we should build quality in with good design and feedback from the field. When we develop, we should do testing together with coding. As we deploy, automated safety checks should give thumbs-up or thumbs-down. Post-deployment, we continue to watch, learn, and adjust.
Conviction #5: Give fast feedback
The acronym CI/CD is ubiquitous in our industry, but I feel like it’s missing something important: “CT”, or Continuous Testing. CI and CD are great for pushing code fast, but without testing, they could be pushing garbage. Testing does not improve quality directly, but continuous revelation of quality helps teams find and resolve issues fast. It demands response. Continuous Testing keeps the DevOps infinity loop safe.
Fast feedback is critical. The sooner and faster teams discover problems, the less pain those problems will cause. Think about it: if a developer is notified that their code change caused a failure within a minute, they can immediately flip back to their code, which is probably still open in an editor. If they find out within an hour, they’ll still have their code fresh in their mind. Within a day, it’ll still be familiar. A week or more later? Fuggedaboutit! Heaven forbid the problem goes undetected until a customer hits it.
Continuous testing enables fast feedback. Automation enables continuous testing. Test automation that isn’t running continuously is worthless because it provides no feedback.
Japanese woodblock printers also relied on fast feedback. If they noticed anything wrong with the prints as they pressed them, they could scrap the misprint and move on. However, since they were meticulous about quality, misprints were rare. Nevertheless, each print was unique because each impression was done manually. The amount, placement, and hue of ink could vary slightly from print to print. Over time, the woodblocks themselves wore down, too.
On the left, the outline around the title is solid, whereas on the right, the outline has breaks. This is because the keyblock had very fine ridges for printing outlines, which suffered the most from wear and tear during repeated impressions. Furthermore, if you look very closely, you can see that the Japanese characters appear bolder on the right than the left. The printer must have used more ink or pressed the title harder for the impression on the right.
Printers would need to spot these issues quickly so they could either correct their action for future prints or warn the publisher that the woodblocks were wearing down. If the print was popular, the publisher could commission a carver to carve new woodblocks to keep production going.
Conviction #6: Go lean
As I’ve said many times now, woodblock printing was a business. Ukiyo-e was commercial art, and competition was fierce. By the 1840s, production peaked with about 250 different publishers. Artists like Hokusai and Hiroshige were rivals. While today we recognize famous prints like The Great Wave, countless other prints were also made.
Publishers competed in a rat race for the best talent and the best prints. They had to be savvy. They had to build good reputations. They needed to respond to market demands for subject material. For example, Kitagawa Utamaro was famous for prints of “female beauties.”
Ukiyo-e artists also took inspiration from each other. If one artist made a popular design, then other artists would copy their style. Here is a print from Hiroshige’s series, Thirty-Six Views of Mount Fuji. That’s right, Hokusai’s biggest rival made his own series of 36 prints about Mount Fuji, and he also made his own version of The Great Wave. If you can’t beat ‘em, join ‘em!
Publishers also had to innovate. Oftentimes, after a print had been in production for a while, they would instruct the printer to change the color scheme. Here are two versions of Hokusai’s Kajikazawa in Kai Province, from Thirty-six Views of Mount Fuji:
The print on the left is an early impression. The only colors used were shades of blue. This was Hokusai’s original artistic intention. However, later prints, like the one on the right, added different colors to the palette. The fishermen now wear red coats. The land has a bokashi green-yellow gradient. The sky incorporates orange tones to contrast the blue. Publishers changed up the colors to squeeze more money out of existing designs without needing to pay artists for new work or carvers for new woodblocks.
However, sometimes when doing this, artistic quality was lost. Compare the fine detail in the land between these two prints. In the early impression, you can see dark blue shading used to pronounce the shadows on the side of the rocks, giving them height and depth, and making the fisherman appear high above the water. However, in the later impression, the green strip of land has almost no shading, making it appear flat and less prominent.
Ukiyo-e publishers would have completely agreed with today’s lean business model. Seek first and foremost to deliver value to your customers. Learn what they want. Try some designs, and if they fail, pivot to something else. When you find what works, get a full end-to-end process in place, and then continuously improve as you go. Respond quickly to changes.
Going lean is very important for software testing, too. Testing is engineering, and it has serious business value. At the same time, testing activities never seem to have as many resources as they should. Testers must be scrappy to deliver valuable quality feedback using the resources they have.
When I think about software testing going lean, I’m not implying that testers should skip tests or skimp on coverage. Rather, I’m saying that world-class systems and processes cannot be built overnight. The most important thing a team can do is build basic end-to-end feedback loops from the start, especially for test automation.
So many times, I’ve seen teams skew their test automation strategy entirely towards implementation. They spend weeks and weeks developing suites of automated tests before they set up any form of Continuous Testing. Instead of triggering tests as part of Continuous Integration, folks must manually push buttons or run commands to make them start. Other folks on the team see results sporadically, if ever. When testers open bug reports, developers might feel surprised.
I recommend teams set up Continuous Testing with feedback loops from the start. As soon as you automate your first test, move onto running it from CI and sending you notifications for results before automating your second test. Close the feedback loop. Start delivering results immediately. As you find hotspots, add more coverage. Talk with developers about the kinds of results they find most valuable. Then, grow your suite once you demonstrate its value. Increase the throughput. Turn those sidewalks into highways. Continue to iteratively improve upon the system as you go. Don’t waste time on tests that don’t matter or dashboards that nobody reads. Going lean means allocating your resources to the most valuable activities. What you’ll find is that success will snowball!
Conviction #7: Open up
Once you have a good thing going, whether it’s woodblock printing or software testing, how can you take it to the next level? Open up! Innovation stalls when you end up staring at your own belly button for too long. Outside influences inspire new creativity.
Ukiyo-e prints had a profound impact on Western art. After Japan opened up to the rest of the world in the mid-1800s, Europeans became fascinated by Japanese art, and European artists began incorporating Japanese styles and subjects into their work. This phenomenon became known as Japonisme. Here, Claude Monet, famous for his impressionist paintings, painted a picture of his wife wearing a kimono with fans adorning the wall behind her:
Vincent van Gogh in particular loved Japanese woodblock prints. He painted his own versions of different prints. Here, we see Hiroshige’s Plum Garden at Kameido side-by-side with Van Gogh’s Flowering Plum Orchard (after Hiroshige):
Van Gogh was drawn to the bold lines and vibrant colors of ukiyo-e prints. There is even speculation that The Great Wave inspired the design of The Starry Night, arguably Van Gogh’s most famous painting:
Notice how the shapes of the waves mirror the shapes of the swirls in the sky. Notice also how deep shades of blue contrast yellows in each. Ukiyo-e prints served as great inspiration for what became known as Modern art in the West.
Influence was also bidirectional. Not only did Japan influence the West, but the West influenced Japan! One thing common to all of the prints in Thirty-six Views of Mount Fuji is the extensive use of blue ink. Prussian blue pigment had recently come to Japan from Europe, and Hokusai’s publisher wanted to make extensive use of the new color to make the prints stand out. Indeed, they did. To this day, Hokusai is renowned for popularizing the deep shades of Prussian blue in ukiyo-e prints.
It’s important in any line of work to be open to new ideas. If Hokusai had not been willing to experiment with new pigments, then we wouldn’t have pieces like The Great Wave.
That’s why I’m a huge proponent of Open Testing. What if we open our tests like we open our source? There are so many great advantages to open source software: helping folks learn, helping folks develop better software, and helping folks become better maintainers. If we become more open in our testing, we can improve the quality of our testing work, and thus also the quality of the software products we are building. Open testing involves many things: building open source test frameworks, getting developers involved in testing, and even publicly sharing test cases and results.
Conviction #8: Show empathy
In this article, we’ve seen lots of great artwork, and we’ve learned lots of valuable lessons from it. I think ukiyo-e prints remain popular today because their subject matter focuses on the beauty of the world. Artists strived to make pieces of the “floating world” tangible for the common people.
Ukiyo-e prints revealed the supple humanity of the Japanese people, like in this print by Utagawa Kunisada:
Ukiyo-e prints also revealed ordinary people living out their lives, like this print from Hokusai’s Thirty-six Views of Mount Fuji:
Art is compelling. And software, like art, is meant for people. Show empathy. Care about your customers. Remember, as a tester, you are advocating for your users. Try to help solve their problems. Do things that matter for them. Build things that actually bring them value. Be thoughtful, mindful, and humble. Don’t be a jerk.
The Golden Conviction
These eight convictions are things I’ve learned the hard way throughout my career:
Focus on behavior
Prioritize on risk
Shift left and right
Give fast feedback
I live and breathe these convictions every day. Whether you are making woodblock prints or running test cases, these principles can help you do your best work.
If I could sum up these eight convictions in one line, it would be this: Be excellent in all things. If you test software, then you are both an artist and an engineer. You have a craft. Do it with excellence.
In the featured image for this article, you see a beautiful front end. It’s probably not the kind of “front end” you expected. It’s the front end of a 1974 Volkswagen Karmann Ghia. The Karmann Ghia was known as the “poor man’s Porsche.” It’s a very special car. It was actually a collaboration project between Wilhelm Karmann, a German automobile manufacturer, and Carrozzeria Ghia, an Italian automobile designer. Ghia designed the body as a work of art, and Karmann put it on the tried-and-true platform of the classic Volkswagen Beetle. When the Volkswagen executives saw it, they couldn’t say no to mass production.
The Karmann Ghia is a perfect symbol of the state of web development today. We strive to make beautiful front ends with reliable platforms supporting them on the back end. Collaboration from both sides is key to success, but what people remember most is the experience they have with your apps. My mom drove a Karmann Ghia like this when she was a teenager, and to this day she still talks about the good times she had with it.
Good quality, design, and experience are indispensable aspects of front ends – whether for classic cars or for the Web. In this article, I’ll share seven major trends I see in front end web testing. While there’s a lot of cool new things happening, I want y’all to keep in mind one main thing: tools and technologies may change, but the fundamentals of testing remain the same. Testing is interaction plus verification. Tests reveal the truth about our code and our features. We do testing as part of development to gather fast feedback for fixes and improvements. All the trends I will share today are rooted in these principles. With good testing, you can make sure your apps will look visually perfect, just like… you know.
#1. End-to-end testing
Here’s our first trend: End-to-end testing has become a three-way battle. For clarity, when I say “end-to-end” testing, I mean black-box test automation that interacts with a live web app in an active browser.
Over the years, though, Selenium has received a lot of criticism. Selenium WebDriver is a low-level protocol. It does not handle waiting automatically, leading many folks to unknowingly write flaky scripts. It requires clunky setup since WebDriver executables must be separately installed. Many developers dislike Selenium because coding with it requires a separate workflow or state of mind from the main apps they are developing.
Cypress was the answer to Selenium’s shortcomings. It aimed to be a modern framework with excellent developer experience, and in a few short years, it quickly became the darling test tool for front end developers. Cypress tests run in the browser side-by-side with the app under test. The syntax is super concise. There’s automatic waiting, meaning less flakiness. There’s visual tracing. There’s API calls. It’s nice. And it took a big chomp out of Selenium’s market share.
Enter Playwright, the new open source test framework from Microsoft. Playwright is the spiritual successor to Puppeteer. It boasts the wide browser and language compatibility of Selenium with the refined developer experience of Cypress. It even has a code generator to help write tests. Plus, Playwright is fast – multiple times faster than Selenium or Cypress.
Playwright is still a newcomer, and it doesn’t yet have the footprint of the other tools. Some folks might be cautious that it uses browser projects instead of stock browsers. Nevertheless, it’s growing fast, and it could be a major contender for the #1 title. In Applitools’ recent Let The Code Speak code battles, Playwright handily beat out both Selenium and Cypress.
All these are good tools to choose (except Protractor). They can handle any kind of web app that you’re building. If you want to learn more about them, Test Automation University has courses for each.
#2. Component testing
End-to-end testing isn’t the only type of testing a team can or should do. Component testing is on the rise because components are on the rise! Many teams now build shareable component libraries to enforce consistency in their web design and to avoid code duplication. Each component is like a “unit of user interface.” Not only do they make development easier, they also make testing easier.
Component testing is distinct from unit testing. A unit test interacts directly with code. It calls a function or method and verifies its outcomes. Since components are inherently visual, they need to be rendered in the browser for proper testing. They might have multiple behaviors, or they may even trigger API calls. However, they can be tested in isolation of other components, so individually, they don’t need full end-to-end tests. That’s why, from a front end perspective, component testing is the new integration testing.
Storybook is a very popular tool for building and testing components in isolation. In Storybook, each component has a set of stories that denote how that component looks and behaves. While developing components, you can render them in the Storybook viewer. You can then manually test the component by interacting with them or changing their settings. Applitools also provides an SDK for automatically running visual tests against a Storybook library.
Cypress is also entering the component testing game. On June 1, 2022, Cypress released version 10, which included component testing support. This is a huge step forward. Before, folks would need to cobble together their own component test framework, usually as an extension of a unit test project or an end-to-end test project. Many solutions just ran automated component tests purely as Node.js processes without any browser component. Now, Cypress makes it natural to exercise component behaviors individually yet visually.
I love this quote from Cypress about their approach to component testing:
This quote hits on something big. So many automated tests fail to interact with apps like real users. They hinge on things like IDs, CSS selectors, and XPaths. They make minimal checks like appearance of certain elements or text. Pages could be completely broken, but automated tests could still pass.
#3. Visual testing
We really want the best of both worlds: the simplicity and sensibility of manual testing with the speed and scalability of automated testing. Historically, this has been a painful tradeoff. Most teams struggle to decide what to automate, what to check manually, and what to skip. I think there is tremendous opportunity in bridging the gap. Modern tools should help us automate human-like sensibilities into our tests, not merely fire events on a page.
That’s why visual testing has become indispensable for front end testing. Web apps are visual encounters. Visuals are the DNA of user experience. Functionality alone is insufficient. Users expect to be wowed. As app creators, we need to make sure those vital visuals are tested. Heaven forbid a button goes missing or our CSS goes sideways. And since we live in a world of continuous development and delivery, we need those visual checkpoints happening continuously at scale. Real human eyes are just too slow.
For example, I could have a login page that has an original version (left) and a changed version (right):
Visual testing tools alert you to meaningful changes and make it easy to compare them side-by-side. They catch things you might miss. Plus, they run just like any other automated test suite. Visual testing was tough in the past because tools merely did pixel-to-pixel comparisons, which generated lots of noise for small changes and environmental differences. Now, with a tool like Applitools Visual AI, visual comparisons accurately pinpoint the changes that matter.
Test automation needs to check visuals these days. Traditional scripts interact with only the basic bones of the page. You could break the layout and remove all styling like this, and there’s a good chance a traditional automated test would still pass:
With visual testing techniques, you can also rethink how you approach cross-browser and cross-device testing. Instead of rerunning full tests against every browser configuration you need, you can run them once and then simply re-render the visual snapshots they capture against different browsers to verify the visuals. You can do this even for browsers that the test framework doesn’t natively support! For example, using a platform like Applitools Ultrafast Test Cloud, you could run Cypress tests against Electron in CI and then perform visual checks in the Cloud against Safari and Internet Explorer, among other browsers. This style of cross-platform testing is faster, more reliable, and less expensive than traditional ways.
#4. Performance testing
Functionality isn’t the only aspect of quality that matters. Performance can make or break user experience. Most people expect any given page to load in a second or two. Back in 2016, Google discovered that half of all people leave a site if it takes longer than 3 seconds to load. As an industry, we’ve put in so much work to make the front end faster. Modern techniques like server-side rendering, hydration, and bloat reduction all aim to improve response times. It’s important to test the performance of our pages to make sure the user experience is tight.
Thankfully, performance testing is easier than ever before. There’s no excuse for not testing performance when it is so vital to success. There are many great ways to get started.
The simplest approach is right in your browser. You can profile any site with Chrome DevTools. Just right click the page, select “Inspect,” and switch to the Performance tab. Then start the profiler and start interacting with the page. Chrome DevTools will capture full metrics as a visual time series so you can explore exactly what happens as you interact with the page. You can also flip over to the Network tab to look for any API calls that take too long. If you want to learn more about this type of performance analysis, Test Automation University offers a course entitled Tools and Techniques for Performance and Load Testing by Amber Race. Amber shows how to get the most value out of that Performance tab.
Another nifty tool that’s also available in Chrome DevTools is Google Lighthouse. Lighthouse is a website auditor. It scores how well your site performs for performance, accessibility, progressive web apps, SEO, and more. It will also provide recommendations for how to improve your scores right within its reports. You can run Lighthouse from the command line or as a Node module instead of from Chrome DevTools as well.
Using Chrome DevTools manually for one-off checks or exploratory testing is helpful, but regular testing needs automation. One really cool way to automate performance checks is using Playwright, the end-to-end test framework I mentioned earlier. In Playwright, you can create a Chrome DevTools Protocol session and gather all the metrics you want. You can do other cool things with profiling and interception. It’s like a backdoor into the browser. Best of all, you could gather these metrics together with functional testing! One framework can meet the needs of both functional and performance test automation.
There’s another curve ball when testing websites: what about machine learning models? For example, whenever you shop at an online store, the bottom of almost every product page has a list of recommendations for similar or complementary products. For example, when I searched Amazon for the latest Pokémon video game, Amazon recommended other games and toys:
Recommendation systems like this might be hard-coded for small stores, but large retailers like Amazon and Walmart use machine learning models to back up their recommendations. Models like this are notoriously difficult to test. How do we know if a recommendation is “good” or “bad”? How do I know if folks who like Pokémon would be enticed to buy a Kirby game or a Zelda game? Lousy recommendations are a lost business opportunity. Other models could have more serious consequences, like introducing harmful biases that affect users.
Machine learning models need separate approaches to testing. It might be tempting to skip data validation because it’s harder than basic functional testing, but that’s a risk not worth taking. To do testing right, separate the functional correctness of the frontend from the validity of data given to it. For example, we could provide mocked data for product recommendations so that tests would have consistent outcomes for verifying visuals. Then, we could test the recommendation system apart from the UI to make sure its answers seem correct. Separating these testing concerns makes each type of test more helpful in figuring out bugs. It also makes machine learning models faster to test, since testers or scripts don’t need to navigate a UI just to exercise them.
If you want to learn more about testing machine learning courses, Carlos Kidman created an excellent course all about it on Test Automation University named Intro to Testing Machine Learning Models. In his course, Carlos shows how to test models for adversarial attacks, behavioral aspects, and unfair biases.
Below is an example snippet of HTML code with HTMX attributes for posting a click and showing the response:
<!-- have a button POST a click via AJAX -->
<button hx-post="/clicked" hx-swap="outerHTML">
#7. Autonomous testing
Finally, there is one more trend I want to share, and this one is more about the future than the present: autonomous testing is coming. Ironically, today’s automated testing is still manually-intensive. Someone needs to figure out features, write down the test steps, develop the scripts, and maintain them when they inevitably break. Visual testing makes verification autonomous because assertions don’t need explicit code, but figuring out the right interactions to exercise features is still a hard problem.
I think the next big advancement for testing and automation will be autonomous testing: tools that autonomously look at an app, figure out what tests should be run, and then run those tests automatically. The key to making this work will be machine learning algorithms that can learn the context of the apps they target for testing. Human testers will need to work together with these tools to make them truly effective. For example, one type of tool could be a test recommendation engine that proposes tests for an app, and the human tester could pick the ones to run.
There’s lots of exciting stuff happening in the world of the front end. As I said before, tools and technologies may change, but fundamentals remain the same. Each of these trends is rooted in tried-and-true principles of testing. They remind us that software quality is a multifaceted challenge, and the best strategy is the one that provides the most value for your project.
So, what do you think? Did I hit all the major front end trends? Did I miss anything? Let me know in the comments!
This article introduces visual testing as a technique that can revolutionize software quality assurance (QA) practices. It is based on a talk I delivered on June 9, 2022 at AITP-RTP, and its target audience includes IT professionals and leaders who may not be hands-on with testing, coding, or automation.
Visual testing techniques are an incredible way to maximize the value of your functional tests. Instead of checking traditional things like text or attributes, visual testing captures full snapshots of your application’s pages and looks for visual differences over time. This isn’t just another nice-to-have feature that’s on the bleeding edge of technology. It’s a tried-and-true technique that anyone can use, and it makes testing easier!
In this article, I want to “open your eyes” to see how visual testing can revolutionize how you approach software quality. I want you to see things in new ways, and I’ll cover five key advantages of visual testing. I’ll use Applitools as the visual testing tool for demonstration. And don’t worry, everything will be high-level – I’ll be light on the code.
What is software testing?
We all know that there are several different kinds of testing. Here’s a short list:
You name it, there’s a test for it. We could play buzzword bingo if we wanted. But what is “testing”? In simplest terms, testing = interaction + verification. That’s it! You do something, and you make sure it works. Every kind of testing reduces to this formula.
We’ve been testing software since the dawn of computers. The “first computer bug” happened on September 9, 1947, when a moth flew into one of the relays of the Mark II computer at Harvard University. What you’re seeing here is Grace Hopper’s bug report, with the dead moth taped onto the notebook page.
Traditional testing practices
Historically, all testing was done manually. Whether it was Grace Hopper pulling a dead moth out of computer relays with tweezers or someone banging on a keyboard to navigate through a desktop app, humans have driven testing. Manual testing was practically the only way to do testing for decades. As applications became more user-centric with the rise of PCs in the 1980s, testing became a much more approachable discipline. Folks didn’t need to hold computer science degrees or to be software engineers to be successful – they just needed common sense and grit. Companies built entire organizations for testers. Releases wouldn’t ship until QA gave them seals of approval. Test repositories could have hundreds, even thousands, of test procedures.
Unfortunately, manual testing does not scale very well. It’s a slow process. If you want to test an app, you need to set everything up, log in, and exercise all the different features. Any time you discover a problem, you need to stop, investigate, and write a report. Every time there’s a new development build, you need to do it all over again. The only way to scale is to hire more testers. Even with more people, testing cycles could take days, weeks, or even months. When I worked at NetApp, the main functional testing phase for a major release took over half a year to complete.
The rise of automation
Then, automation came. It started becoming popular with unit testing for functions and methods directly in the code itself in the late 1990s, but then black box automation tools and frameworks started becoming popular in the mid 2000s. Instead of manually performing test cases step by step, testers would write scripts to automatically execute test steps.
Automation offered several benefits over manual testing. With automation, you could run tests more quickly. Scripts don’t need to wait for humans to react to pages or write down results. You could also run tests more frequently. Teams started running tests continuously – nightly at first, and then after every code change. These benefits enabled teams to widen their test coverage and provide faster feedback. Testing work that would take a full team days to complete could be finished in a matter of hours, if not minutes. Test results would be posted in real time instead of at the end of testing cycles. Instead of endlessly executing tests manually, testers gained time back to work on other things, like automating even more tests or doing exploratory testing activities.
Challenges with automation
Unfortunately, it wasn’t all rainbows and unicorns. Test automation was hard to develop. Since it was inherently more complex than manual testing, it required more skills. Testers needed to learn how to use tools like Selenium or Postman. On top of that, they needed to learn how to do programming. If they wanted to use codeless tools instead, then their companies probably had to shell out a pretty penny for licenses. Regardless of the tools chosen, automated scripts could never be made perfect. They are inherently fragile because they depend directly upon the features under test. For example, if a button on a web page changes, then the script will crash. Automated tests also gained a reputation for being flaky when testers didn’t appropriately handle waiting for things on the page to load. Furthermore, automation was only suitable for checking low-level things like text and numbers. That’s fine for unit tests and API tests, but it’s not suitable for user interfaces that are inherently visual. Passing tests could miss a lot of problems, giving a false sense of security.
When considering all these challenges together, we discovered as an industry that test automation isn’t fully autonomous. Despite dreaming of testing-made-easy, automation just made things harder. Teams who could build good test automation projects reaped handsome returns, but for many, the bar was too high. It was out of reach. Many tried and failed. Trust me, I’ve talked with lots of folks who struggle with test automation.
What we really want is the best of both worlds. We want the simplicity and sensibility of manual testing, but with the speed and scalability of automated testing. To get both, most teams use a split testing strategy. They automate some tests while running others manually. Actually, I’ve commonly seen teams run all their tests manually and then automate whatever they can with the time they have left. Some teams are more forward with their automation work, but not all. Folks perpetually make tradeoffs.
But, what if there was a way to get the simplicity and sensibility of manual testing with automation? What if automation could visually inspect our applications for differences like a human could?
Walking through an example
Consider a basic web application with a standard login page:
When we look at this from top to bottom, we see:
A page title
A username field
A password field
A sign-in button
A remember-me checkbox
Links to social media
However, during the course of development, we know things change – for better or worse. Here’s a different version of the same page:
Can you spot the differences? Looking at these two pages side-by-side makes comparison easier:
The logos are different, and the sign-in buttons are different. While I’d probably ask the developers about the sign-in button change, I’d categorically consider that logo change a bug. My gut tells me a human tester would catch these differences if they were paying attention, but there’s a chance they could miss them. Traditional automation would most likely fly right by these changes without stopping.
In fact, pages can be radically broken visually yet still have passing automated tests. In this version, I stripped all the CSS off the page:
We would definitely call this page broken. A traditional functional test script hinges on the most basic functionality of web pages, like IDs and element attributes. If it clicks, it works! It completely misses visuals. I even wrote a short test script with basic assertions, and sure enough, it passed on all three versions of this login page. Those are huge test gaps.
The magic of visual testing
So, what if we could visually inspect this page with automation? That would easily catch any changes that human eyes would detect, but with speed and scale. We could take a baseline snapshot that we consider “good,” and every time we run our tests, we take a new “checkpoint” snapshot. Then, we can compare the two side-by-side to detect any changes. This is what we call visual testing: take a baseline snapshot to start, take a checkpoint snapshot after every change, and look for any visual differences programmatically. If a picture is worth a thousand words, then a snapshot is worth a thousand assertions.
One visual snapshot captures everything on the page. As a tester, you don’t need to explicitly state what to check: a snapshot implicitly covers layout, color, size, shape, and styling. That’s a huge advantage over traditional functional test automation.
Unfortunately, not all visual testing techniques are created equal. Programming a tool to capture snapshots and perform pixel-by-pixel comparisons isn’t too difficult, but determining if those changes matter is very difficult. A good visual testing tool should ignore changes that don’t matter – like small padding differences – and focus on changes that do matter – like missing elements. Otherwise, human testers will need to review every single result, nullifying any benefit of automating visual tests.
Take a look at these two pictures. They show a cute underwater scene. There are a total of ten differences between the two pictures. Can you find them?
Unfortunately, a pixel-to-pixel comparison won’t find any of them. I ran these two pictures through Applitools Eyes using an exact pixel-to-pixel comparison, and this is what happened:
Except for the whitespace on the sides, every pixel was different. As humans, we can clearly see that these images are very similar, but because they were a few pixels off on the sides, automation failed to pinpoint meaningful differences.
This is where AI really helps. Applitools uses Visual AI to detect meaningful changes that humans would see and ignore inconsequential differences that just make noise. Here, I used Applitools’ “strict” comparison, which pinpointed each of the ten differences:
That’s the second advantage of good automated visual testing: Visual AI focuses on meaningful changes to avoid noise. Visual test results shouldn’t waste testers’ time over small pixel shifts or things a human wouldn’t even notice. They should highlight what matters, like missing elements, different colors, or skewed layouts. Visual AI is a differentiator for visual testing tools. Not all tools rise above pixel-to-pixel comparisons.
Simplifying test cases
The second path to automation is using codeless tools. Codeless tools don’t require testers to have programming skills. Instead, they record testers as they exercise features under test, and then they can replay those recorded tests at the push of a button. Most codeless tools also have some sort of visual builder through which testers can tweak and update their tests. There are several codeless tools available on the market, and many of them require paid licenses. However, Selenium IDE is a free and open source tool that does the job quite nicely.
Coded and codeless tools serve different needs. Coded tools are great for folks like me who know how to code and want high-power, customizable automation. Codeless tools are great for teams that are just getting started with automation, especially when most of their testing has historically been done manually. Regardless of approach, the good news is that you can do visual testing either way! For example, if you use Applitools, then there are SDKs and integrations for many different tools and frameworks.
As we recall, testing is interaction plus verification. When automating tests, the interactions and the verifications are scripted using either a coded or codeless tool. Testers must specify each of those operations. For example, if a test is exercising login behavior on this login page:
Then the interactions would be:
Loading the page
Clicking the login button
Waiting for the main page to load
And then, the verifications would be checking that the main page loads correctly:
As we can see, this main page has lots of stuff on it. We could check several things:
The title bar at the top
The side bar with different card types and lending options
The warning message about nearby branches closing soon
The values in the financial overview
The table of recent transactions
But, what should we check? The more things we verify in a test, the more coverage the test will have. However, the test will take longer to develop, require more time to run, and have a higher risk of breaking as development proceeds.
I wrote some Java code to perform high-level assertions on this page:
// Check various page elements
waitForAppearance(By.cssSelector("div.element-search.autosuggest-search-activator > input"));
// Check time message
"Your nearest branch closes in:( \\d+[hms])+",
// Check menu element names
var menuElements = driver.findElements(By.cssSelector("ul.main-menu li span"));
var menuItems = menuElements.stream().map(i -> i.getText().toLowerCase()).toList();
var expected = Arrays.asList("card types", "credit cards", "debit cards", "lending", "loans", "mortgages");
// Check transaction statuses
var statusElements = driver.findElements(By.xpath("//td[./span[contains(@class, 'status-pill')]]/span"));
var statusNames = statusElements.stream().map(n -> n.getText().toLowerCase()).toList();
var acceptableNames = Arrays.asList("complete", "pending", "declined");
If you don’t know Java, please don’t be frightened by this code! It checks that certain elements and links appear, that the warning message displays a timeframe, and that correct names for menu items and transaction statuses appear. As you can see, that’s a lot of complicated code – and that’s what I want you to see.
Sadly, its coverage is quite shallow. This code doesn’t check the placement of any elements. It doesn’t check the title bar, the financial overview values, or any transaction values other than status. If I wanted to cover all these things, I’d probably need to add at least another hundred lines of code. That might take me an hour to find all the locators, parse the text values, and run it a few times to make sure it works. Someone else would need to do a code review before the changes could be merged, as well.
If I do visual testing, then I could eliminate all this code with a one-line snapshot call:
As an engineer, I cannot overstate how much this simplifies test development. A single snapshot implicitly covers everything on the page: visuals, text, placement, and color. I don’t need to make tradeoffs about what to check and what not to check. Visual snapshots remove a tremendous cognitive burden. They improve test coverage and make tests more robust. This is the same whether you are using a coded tool like Selenium WebDriver in Java or a codeless tool like Selenium IDE.
This is the third major advantage visual testing has over traditional functional testing: visual snapshots greatly simplify assertions. Instead of spending hours deciding what to check, figuring out locators, and writing transformation logic, you can make one concise snapshot call and be done. I said it before, and I’ll say it again: If a picture is worth a thousand words, then a snapshot is worth a thousand assertions.
Testing different browsers and devices
So, what about cross-browser and cross-device testing? It’s great if my app works on my machine, but it also needs to work on everyone else’s machine. The major browsers these days are Chrome, Edge, Firefox, and Safari. The two main mobile platforms are iOS and Android. That might not sound like too much hassle at first, but then consider:
All the versions of each browser – typically, you want to verify that your app works on the last two or three releases.
All the screen sizes – modern web apps have responsive designs that change based on viewport.
All the device types – desktops and laptops have various operating systems, and phones and tablets come in a plethora of models.
We have a combinatorial explosion! Traditional functional tests must be run start-to-finish in their entirety on each of these platforms. Most teams will pick a few of the most popular combinations to test and skip the rest, but that could still require lots of test execution.
Snapshots are more powerful than screenshots because snapshots can be re-rendered. For example, I could run my test one time on my local machine using Google Chrome, and then I could re-render any snapshots I capture from that test on Firefox, Safari, or Edge. I wouldn’t need to run the test from start to finish three more times – I just need to re-render the snapshots in the new browsers and run the Visual AI checker. I could re-render them using different versions and screen sizes, too, because I have the full page, not just a flat screenshot. This works for web apps as well as mobile apps.
Visually-based cross-platform testing is lightning fast. A typical UI test case takes about a minute to run. It could be more or less, but from my experience, 1 minute is a rough industry average. A visual checkpoint backed by Visual AI takes only a few seconds to complete. Do the math: if you have a large test suite with hundreds to thousands of tests that you need to test across multiple configurations, then visual testing could save you hours, if not days, of test execution time per cycle. Plus, if you use a service like Applitools Ultrafast Test Cloud, then you won’t need to set up all those different configurations yourself. You’ll spend less time and money on your full test efforts.
When to start visual testing
There is one more thing I want y’all to consider: when should a team adopt visual testing into their quality strategy? I can’t tell you how many times folks have told me, “Andy, that visual testing thing looks so cool and so helpful, but I don’t think my team will ever get there. We’re just getting started, and we’re new to automation, and automation is so hard, and I don’t think we’ll ever be mature enough to adopt visual testing techniques.” Every time I hear these reasons, I can’t help but do a facepalm.
Visual testing makes automation easier:
It makes verifications much easier to perform.
Visual snapshots cover more of a view than traditional assertions ever could.
Visual AI ensures that any visual differences identified are important.
Re-rendering snapshots on different configurations simplifies cross-platform testing.
I really think teams should do visual testing from the start. Consider this strategy: start by automating a few basic tests that navigate to different pages of an app and capture snapshots of each. The interactions would be straightforward, and the verifications would be single-step one-liners. If the testers are new to automation, they could go codeless with Selenium IDE just to get started. That would provide an immense amount of value for relatively little automation work. It’s the 80/20 rule: 80% of the value for 20% of the work. Then, later, when the team has more time or more maturity, they can expand the automation project with larger tests that use both traditional and visual assertions.
Test automation is hard, no matter what tool or what language you use. Teams struggle to automate tests in time and to keep them running. Visual testing simplifies implementation and execution while catching more problems. It offers the advantage of making functional testing easier. It’s not a technique only for those on the bleeding edge. It’s here today, and it’s accessible to anyone doing test automation.
Overall, visual testing is a winning strategy. It has several advantages over traditional functional testing. Please note, however, that visual testing does not replace functional testing. Instead, it supercharges it. With a visual testing tool like Applitools Eyes, you can do visual testing in any major language or test framework you like, and with Applitools Ultrafast Test Cloud, you can do visual testing using any major browser or mobile configuration.
I’m super excited to introduce a somewhat new idea to you and to our industry: Open Testing: What if we open our tests like we open our source? I’m not merely talking about creating open source test frameworks. I’m talking about opening the tests themselves. What if it became normal to share test cases and automated procedures? What if it became normal for companies to publicly share their test results? And what are the degrees of openness in testing for which we should strive as an industry?
I think that we – whether we are testers, developers, managers, or any other role in software – can greatly improve the quality of our work if we adopt principles of openness into our testing practices. To help me explain, I’d like to share how I learned about the main benefits of open source software, and then we can cross those benefits over into testing work.
So, let’s go way back in time to when I first encountered open source software.
My first encounter with open source code
I first started programming when I was in high school. At 13 years old, I was an incoming freshman at Parkville High School in their magnet school for math, science, and computer science in good old Baltimore, Maryland. (Fun fact: Parkville’s mascots were the Knights, which is my last name!) All students in the magnet program needed to have a TI-83 Plus graphing calculator. Now, mind you, this was back in the day before smart phones existed. Flip phones were the cool trend! The TI-83 Plus was cutting-edge handheld technology at that time. It was so advanced that when I first got it, it took me 5 minutes to figure out how to turn it off!
I quickly learned that the TI-83 Plus was just a mini-computer in disguise. Did you know that this thing has a full programming language built into it? TI-BASIC! Within the first two weeks of my freshman Intro to Computer Science class, our teacher taught us how to program math formulas: Slope. Circle circumference and area. The quadratic formula. You name it, I programmed it, even if it wasn’t a homework assignment. It felt awesome! It was more fun to me than playing video games, and believe me, I was a huge Nintendo fan.
There were two extra features of the TI-83 Plus that made it ideal for programming. First, it had a link cable for sharing programs. Two people could connect their calculators and copy programs from one to the other. Needless to say, with all my formulas, I became quite popular around test time. Second, anyone could open any program file on the calculator and read its code. The TI-BASIC source code could not be hidden. By design, it was “open source.”
This is how I learned my very first lesson about open source software: Open source helps me learn. Whenever I would copy programs from others, including games, I would open the program and read the code to see how it worked. Sometimes, I would make changes to improve it. More importantly, though, many times, I would learn something new that would help me write better programs. This is how I taught myself to code. All on this tiny screen. All through ripping open other people’s code and learning it. All because the code was open to me.
From the moment I wrote my first calculator program, I knew I wanted to become a software engineer. I had that spark.
My first open source library
Let’s fast-forward to college. I entered the Computer Science program at Rochester Institute of Technology – Go Tigers! By my freshman year in college, I had learned Java, C++, a little Python, and, of all things, COBOL. All the code in all my projects until that point had been written entirely by me. Sometimes, I would look at examples in books as a guide, but I’d never use other people’s code. In fact, if a professor caught you using copied code, then you’d fail that assignment and risk being expelled from the school.
Then, in my first software engineering course, we learned how to write unit tests using a library called JUnit. We downloaded JUnit from somewhere online – this was before Maven became big – and hooked it into our Java path. Then, we started writing test classes with test case methods, and somehow, it all ran magically in ways I couldn’t figure out at the time.
I was astounded that I could use software that I didn’t write myself in a project. Permission from a professor was one thing, but the fact that someone out there in the world was giving away good code for free just blew my mind. I saw the value in unit tests, and I immediately saw the value in a simple, free test framework like JUnit.
That’s when I learned my second lesson about open source software: Open source helps me become a better developer. I could have written my own test framework, but that would have taken me a lot of time. JUnit was ready to go and free to use. Plus, since several individuals had already spent years developing JUnit, it would have more features and fewer bugs than anything I could develop on my own for a college project. Using a package like JUnit helped me write and run my unit tests without needing to become an expert in test automation frameworks. I could build cool things without needing to build every single component.
That revelation felt empowering. Within a few years of taking that software engineering course, sites for hosting open source projects like GitHub became huge. Programming language package indexes like Maven, NuGet, PyPI, and NPM became development mainstays. The running joke within Python became that you could import anything! This was way better than swapping calculator games with link cables.
Then, I got the chance to give back: I launched an open source project called Boa Constrictor. Boa Constrictor is a .NET implementation of the Screenplay Pattern. It helps you make better interactions for better automation. Out of the box, it provides Web UI interactions using Selenium WebDriver and Rest API interactions using RestSharp, but you can use it to implement any interactions you want.
My company and I released Boa Constrictor publicly in October 2020. You can check out the boa-constrictor repository on GitHub. Originally, my team and I at Q2 developed all the code. We released it as an open source project hoping that it could help others in the industry. But then, something cool happened: folks in the industry helped us! We started receiving pull requests for new features. In fact, we even started using some new interactions developed by community members internally in our company’s test automation project. We also proudly participated in Hacktoberfest in 2020 and 2021.
That’s when I learned my third lesson about open source software: Open source helps me become a better maintainer. Large projects need all the help they can get. Even a team of core maintainers can’t always handle all the work. However, when a project is open source, anyone who uses it can help out. Each little contribution can add value for the whole user base. Maintaining software then becomes easier, and the project can become more impactful.
Struggling with poor quality
As a Software Engineer in Test, I found myself caught between two worlds. In one world, I was a developer at heart who loved to write code to solve problems. In the other world, I was a software quality professional who tested software and advocated for improvements. These worlds came together primarily through test automation and continuous integration. Now that I’m a developer advocate, I still occupy this intersectionality with a greater responsibility for helping others.
However, throughout my entire career, I keep hitting one major problem: Software quality has a problem with quality. Let that sink in: software quality has a big problem with quality. I’ve worked on teams with titles ranging from “Software Quality Assurance” to “Test Engineering & Architecture,” and even an “Automation Center of Excellence.” Despite the titular focus on quality, every team has suffered from aspects of poor quality in workmanship.
Here are a few poignant examples:
Manual test case repositories are full of tests with redundant steps.
Test automation projects are riddled with duplicate code.
Setup and cleanup steps are copy-pasted endlessly, whether needed or not.
Automation code uses poor practices, such as global variables instead of dependency injection.
A 90% success rate is treated as a “good” day with “limited” flakiness.
Many tests cover silly, pointless, or unimportant things instead of valuable, meaningful behaviors.
How can we call ourselves quality professionals when our own work suffers from poor quality? Why are these kinds of problems so pervasive? I think they build up over time. Copy-pasting one procedure feels innocuous. One rogue variable won’t be noticed. One flaky test is no big deal. Once this starts happening, teams insularly keep repeating these practices until they make a mess. I don’t think giving teams more time to work on these problems will solve them, either, because more time does not interrupt inertia – it merely prolongs it.
The developer in me desperately wants to solve these problems. But how? I can do it in my own projects, but because my tests are sealed behind company doors, I can’t use it to show others how to do it at scale. Many of the articles and courses we have on how-to-do-X are full of toy examples, too.
Changing our quality culture
So, how do we get teams to break bad habits? I think our industry needs a culture change. If we could be more open with testing like we are open with source code, then perhaps we could bring many of the benefits we see from open source into testing:
Helping people learn testing
Helping people become better testers
Helping people become better test maintainers
If we cultivate a culture of openness, then we could lead better practices by example. Furthermore, if we become transparent about our quality, it could bolster our users’ confidence in our products while simultaneously keeping us motivated to keep quality high.
There are multiple ways to start pursuing this idea of open testing. Not every possibility may be applicable for every circumstance, but my goal is to get y’all thinking about it. Hopefully, these ideas can inspire better practices for better quality.
Openness through internal collaboration
For a starting point of reference, let’s consider the least open context for testing. Imagine a team where testing work is entirely siloed by role. In this type of team, there is a harsh line between developers and testers. Only the testers ever see test cases, access test repositories, or touch automation. Test cases and test plans are essentially “closed” to non-testers due to access, readability, or even apathy. The only output from testers are failure percentages and bug reports. Results are based more on trust than on evidence.
This kind of team sounds pretty bleak. I hope this isn’t the kind of team you’re on, but maybe it is. Let’s see how openness can make things better.
The first step towards open testing is internal openness. Let’s break down some siloes. Testers don’t exclusively own quality. Not everyone needs to be a tester by title, but everyone on the team should become quality-conscious. In fact, any software development team has three main roles: Business, Development, and Testing. Business looks for what problems to solve, Development addresses how to implement solutions, and Testing provides feedback on the solution. These three roles together are known as “The Three Amigos” or “The Three Hats.”
Each role offers a valuable perspective with unique expertise. When the Three Amigos stay apart, features under development don’t have the benefit of multiple perspectives. They might have serious design flaws, they might be unreasonable to implement, or they might be difficult to test. Misunderstandings could also cause developers to build the wrong things or testers to write useless tests. However, when the Three Amigos get together, they can jointly contribute to the design of product features. Everyone can get on the same page. The team can build quality into the product from the start. They could do activities like Question Storming and Example Mapping to help them define behaviors.
As part of this collaboration, not everyone may end up writing tests, but everyone will be thinking about quality. Testing then becomes easier because expected behaviors are well-defined and well-understood. Testers get deeper insight into what is important to cover. When testers share results and open bugs, other team members are more receptive because the feedback is more meaningful and more valuable.
We practiced Three Amigos collaboration at my previous company, Q2. My friend Steve was a developer who saw the value in Example Mapping. Many times, he’d pick up poorly-defined user stories with conflicting information or missing acceptance criteria. Sometimes, he’d burn a whole sprint just trying to figure things out! Once he learned about Example Mapping, he started setting up half-hour sessions with the other two Amigos (one of whom was me) to better understand user stories from the start. He got into it. Thanks to proactive collaboration, he could develop the stories more smoothly. One time, I remember we stopped working on a story because we couldn’t justify its business value, which saved Steve two weeks of pointless work. The story didn’t end there: Steve became a Software Engineer in Test! He shifted left so hard that he shifted into a whole new role.
Openness through living specs
Another step towards open testing is living documentation through specification by example. Collaboration like we saw with the Three Amigos is great, but the value it provides can be fleeting if it is not written down. Teams need artifacts to record designs, examples, and eventually test cases.
One reason why I love Example Mapping is because it facilitates a team to spell out stories, rules, examples, and questions onto color-coded cards that they can keep for future refinement.
Stories become work items.
Rules become acceptance criteria.
Examples become test cases.
Questions become spikes or future stories.
During Example Mapping, folks typically write cards quickly. An example card describes a behavior to test, but it might not carefully design the scenario. It needs further refinement. Defining behaviors using a clear, concise format like Given-When-Then makes behaviors easy to understand and easy to test.
For example, let’s say we wanted to test a web search engine. The example could be to search for a phrase like”panda”. We could write this example as the following scenario:
Given the search engine page is displayed
When the user searches for the phrase “panda”
Then the results page shows a list of links for “panda”
This special Given-When-Then format is known as the Gherkin language. Gherkin comes from Behavior-Driven Development tools like Cucumber, but it can be helpful for any type of testing. Gherkin defines testable behaviors in a concise way that follows the Arrange-Act-Assert pattern. You set things up, you interact with the feature, and you verify the outcomes.
Furthermore, Gherkin encourages Specification by Example. This scenario provides clear instructions on how to perform a search. It has real data, which is the search phrase “panda,” and clear results. Using real-world examples in specifications like this helps all Three Amigos understand the precise behavior.
Behavior specifications are multifaceted artifacts:
They are requirements that define how a feature should behave.
They are acceptance criteria that must be met for a deliverable to be complete.
They are test cases with clear instructions.
They could become automated scripts with the right kind of test framework.
They are living documentation for the product.
Living documentation is open and powerful. Anyone on the team or outside the team can read it to learn about the product. Refining ideas into example cards into behavior specs becomes a pipeline that delivers living doc as a byproduct of the software development lifecycle.
SpecFlow is one of the best frameworks that supports this type of openness with Specification by Example and Living Documentation. SpecFlow is a free and open-source test automation framework for .NET. In SpecFlow, you write your test cases as Gherkin scenarios, and you automate each Given-When-Then step using C# methods.
One of SpecFlow’s niftiest features, however, is SpecFlow+ LivingDoc. Most test frameworks focus exclusively on automation code. When a test is automated, then only a programmer can read it and understand it. Gherkin makes this easier because steps are written in plain language, but Gherkin scenarios are nevertheless stored in a code repository that’s inaccessible to many team members. SpecFlow+ LivingDoc breaks that pattern. It turns Gherkin scenarios into a searchable doc site accessible to all Three Amigos. It makes test cases and test automation much more open. LivingDoc also provides test results for each scenario. Green check marks indicate passing tests, while red X’s indicate failures.
Historically, testers use reports like this to provide feedback in-house to their managers and developers. Results indicate what works and what needs to be fixed. However, test results can be useful to more people than just internal team members. What if test results were shared with users and customers? I’m going to repeat that statement, because it might seem shocking: What if users and customers could see test results?
Think about it. Open test results have very positive effects. Transparency with users builds trust. If users can see that things are tested and working, then they will gain confidence in the quality of the product. If they could peer into the living documentation, then they could learn how to use the product even better. On the flip side, transparency holds development teams accountable to keeping quality high, both in the product and in the testing. Open test results offer these benefits only if the results can be trusted. If tests are useless or failures are rampant, then public test results could actually hurt the ones developing the product.
This type of radical transparency would require an enormous culture shift. It may not be appropriate for every company to create public dashboards with their test results, but it could be a strategic differentiator when used wisely. For example, when I worked at Q2, we shared LivingDoc reports with specific PrecisionLender customers after every two-week release. It built trust. Plus, since the LivingDoc report includes only high-level behavior specs with simple results, even a vice president could read it! We could share tests without sharing automation code. That was powerful.
Openness through open source
Let’s keep extending open testing outward. In addition to sharing test results and living documentation, folks can also share tools, frameworks, and other parts of their tests. This is where open testing truly is open source.
We already covered a bunch of open source projects for test automation. As an industry, we are truly blessed with so many incredible projects. Every single one of them represents a team of testers who not only solved a problem but decided to share their solution with the world. Each solution is abstract enough to apply to many circumstances but concrete enough to provide a helpful implementation. Collectively, the projects on this page have probably been downloaded more than a billion times, and that’s no joke. And if you want, you could read the open source code for any of them.
Cool new projects appear all the time, too. One of my favorite projects that started in the past few years is Playwright, an awesome browser automation tool from Microsoft. Playwright makes end-to-end web testing easy, reliable, and fast. It provides cross-browser and cross-language support like Selenium, a concise syntax like Cypress, and a bunch of advanced features like automatic waiting, tracing, and code generation. Plus, Playwright is magnitudes faster than other automation tools. It took things that made Selenium, Cypress, and Puppeteer great, and it took them to the next level.
Openness through shared test suites
So far, all the ways of approaching open testing are things we could do today. Many of us are probably already doing these things, even if we didn’t think of them under the phrase “open testing.” But where can these ideas go in the future?
My mind goes back to one of the big problems with testing that I mentioned earlier: duplication. Opening up collaboration fixes some bad habits, and sharing components eliminates some duplication in the plumbing of test automation, but so many of our tests across the industry repeat the same kinds of steps and follow the same types of patterns.
For example, think about any time you’ve ordered something from an online store. It could be Amazon, Walmart, Target – whatever. Every single online store has a virtual shopping cart. Whenever you want to buy something, you add it to your cart. Then, when you’re done shopping, you proceed to pay for all the items in your cart. If you decide you don’t want something anymore, you remove it from the cart. Easy-peasy.
As I describe this type of shopping cart, I don’t need to show you screenshots from the store website to explain it. Y’all have done so much online shopping that you intuitively know how it works, regardless of the store. Heck, I recently ordered a bunch of parts for an old Volkswagen Beetle from a site named JBugs, and the shopping cart was the same.
If so many applications have the same parts, then why do we keep duplicating the same tests in different places? Think about it. Think about how many times different teams have written nearly identical shopping cart tests. Ouch. Think about how much time was wasted on that duplication of effort.
I think this is something where Artificial Intelligence and Machine Learning could help. What if we could develop machine learning models to learn common behaviors for apps and services? The learning agents would look for things like standard icons and typical workflows. We could essentially create test suites for things like login, search, shopping, and payment that could run successfully on most apps. These kinds of tests probably couldn’t cover everything in any given application, but they could cover basic, common behaviors. Maybe that could cover a quarter of all behaviors worth testing. Maybe a third? Maybe half? Every little bit helps!
Now, imagine sharing those generic test suites publicly. In the same way developers have open source projects to help expedite their coding, and in the same way data scientists have open data sets to use for modeling, testers could have open test suites that they could pick up and run as applicable. Not test tools – but actual runnable tests that could run against any application. If these kinds of test suites prove to be valuable, then prominent ones could become universally-accepted bars of quality for software apps. For example, in the future, companies could download and execute tests that run on any system for the apps they’re developing in addition to the tests they develop in-house. I think that could be a really cool opportunity.
This type of testing – Autonomous Testing – is the future. Software developer and testers will use AI-backed tools to better learn, explore, and exercise app behaviors. These tools will make it easier than ever to automate scriptable tests.
How to start pursuing openness
As we have covered, open testing could take many forms:
It could be openness in collaboration to build better quality from the start.
It could be openness in specification by example and living documentation.
It could be openness in sharing tests and their results with customers and users.
It could be openness in sharing tools, frameworks, and platforms.
It could be openness in building shared test sets for common application behaviors.
Some of these ideas might seem far-fetched or aspirational, but quite honestly, I think each of them could add lots of value to testing practices. I think every tester and every team should look at this list and ask themselves, “Could we try some of these things?” Perhaps your team could take baby steps with better collaboration or better specification. Perhaps your team has a cool project you built in-house that you could release as an open source project, like my old team and I did with Boa Constrictor. Perhaps there’s a startup idea in using machine learning for autonomous testing. Perhaps there are other ways to achieve open testing that aren’t listed here. Who knows? It could be cool!
We should also consider the flip side. Are there certain aspects of testing that should remain closed? My mind goes to security. Could fully open testing inadvertently reveal security vulnerabilities? Could lack of coverage in some areas welcome expedited exploitation? I don’t know, but I think we should consider possibilities like these.
If you want to pursue open testing, here are three questions to get you started:
How is your testing today?
In what ways is it already open?
In what ways is it closed?
How could your testing improve with incremental openness?
We’re talking baby steps here – small improvements that you could easily achieve today.
It could be as small as trying Example Mapping or joining a mob programming session.
How could your testing improve with radical openness?
Shoot the moon! Dream big! Get creative!
In the world of software, anything is possible.
We should also remember that open testing isn’t a goal unto itself. It’s a means to an end, and that end is higher quality: quality in our practices, quality in our artifacts, and ultimately quality in the software we create. We shouldn’t seek openness in testing just because I’m spouting lots of buzzwords in this article. At the same time, we also shouldn’t brush off these ideas as too radical or idealistic. What we should do is seek ways for perpetual improvement. Remember that this whole idea of open testing came from the tried-and-true benefits of open source code.
One of the biggest struggles in learning how to develop top-notch automation is practice. Testing is as much an art as it is a science. It takes time to discern when to add explicit waits, how to craft robust locators, and why to verify one element over another. It also requires apps with specific elements or endpoints to try certain operations. Unfortunately, although resources for learning how to automate tests (like Test Automation University) are abundant these days, public demo web sites for practicing remain elusive. I’ve struggled to find ones that I like, and others frequently ask me for recommendations.
Which demo sites are good?
Below is a list of demo sites I’ve found either by searching online or through recommendations from friends. Many of these sites appear on other “Top N Demo Sites for Testing” articles as well. My list aims not only to provide links to popular demo sites but also to provide recommendations on how to use them.
Types of sites:
Web UI site – looks like a real web site
Web UI elements – tutorial pages showcasing web element types
Mobile UI site – looks like a real mobile site
API site – provides public APIs for testing
DIY – “do it yourself”; you must set up and run the demo site yourself
Of course, the others are still good. That’s why they made the list!
Why bother with “fake” sites?
You might be wondering, “Why do we need demo sites? Why don’t we just use real sites?” Well, demo or “fake” web sites meet a few needs that real sites cannot:
Demo sites offer consistency. They are implemented one way and then do not change. Folks can trust that their tests will always work on them.
Demo sites are often simpler than real sites. They feel less intimidating for newcomers.
Demo sites can be designed for instruction. If they are part of a tutorial, the author can add features to the site to demonstrate concepts.
Demo sites are safer for publications like articles, tutorials, and books. Written content is static, so any sites referenced by examples should also be static. Real sites could change.
Real sites might require end user agreements that forbid automated requests. Some may even throttle or block requests if they suspect the requests come from a “bot.”
Real sites might also bear legal or copyright implications, especially if a company is using the sites for their own content.
What limitations do demo sites have?
Unfortunately, demo sites do have limitations:
Demo sites may be too simplified. They may lack large workflows or real-world data. Folks might become frustrated when they discover inactive elements that appear to be real.
Demo sites may not be built to scale. High request volume or parallel testing scale might cripple them.
Demo sites may seem like they have poor quality, whether or not they actually have poor quality. Sometimes, they are built quickly for testing purposes and therefore don’t have the attention to detail as real sites.
Demo sites with significant company branding may be inappropriate to use. For example, if A and B are competitors, then A shouldn’t use B’s demo site for their product tutorials.
Other great lists
Here are other great articles that list demo sites for testing. I referred to them while compiling this list:
On December 1, 2021, I delivered a workshop on Playwright for TAU: The Homecoming. In my workshop, I taught how to build a test automation project in Python using Playwright with pytest, Python’s most popular test framework. We automated a test case together for performing a DuckDuckGo web search.
If you missed the workshop, no worries: You can still take the workshop as a self-guided tutorial! The workshop instructions and example code are located in this GitHub repository:
Boa Constrictor is the .NET Screenplay Pattern. It helps you make better interactions for better automation! Its primary use case is Web UI and REST API test automation, but it can be used to automate any kind of interactions. The Screenplay Pattern is much more scalable for development and execution than the Page Object Model.
The Boa Constrictor maintainers and I strongly support open source software. That’s why we participated in Hacktoberfest 2021. In fact, this was the second Hacktoberfest we did. We launched Boa Constrictor as an open source project a year ago during Hacktoberfest 2020! We love sharing our code with the community and inspiring others to get involved. To encourage participation this year, we added the “hacktoberfest” label to open issues, and we offered cool stickers to anyone who contributed.
Hacktoberfest 2021 was a tremendous success for Boa Constrictor. Even though the project is small, we received several contributions. Here’s a summary of all the new stuff we added to Boa Constrictor:
Updated WebDriver interactions to use Selenium WebDriver 4.0
Implemented asynchronous programming for Tasks and Questions
Extended the Wait Task to wait for multiple Questions using AND and OR logic
Standardized ToString methods for all WebDriver interactions
Automated unit tests for WebDriver Questions
Wrote new user guides for test framework integrations and interaction patterns
Made small refinements to the doc site
Created GitHub templates for issues and pull requests
Replaced the symbols NuGet package with embedded debugging
Added the README to the NuGet package
Added Shields to the README
Restructured projects for docs, logos, and talk
During Hacktoberfest 2021, we made a series of four releases because we believe in lean development that puts new features in the hands of developers ASAP. The final capstone release was version 2.0.0: a culmination of all Hacktoberfest work! Here’s a view of the Boa Constrictor NuGet package with its new README (Shields included):
If you like project stats, then here’s a breakdown of the contributions by numbers:
11 total contributors (5 submitting more than one pull request)
41 pull requests closed
151 commits made
Over 10K new lines of code
GitHub’s Code Frequency graph for Boa Constrictor shown below illustrates how much activity the project had during Hacktoberfest 2021. Notice the huge green and red spikes on the right side of the chart corresponding to the month of October 2021. That’s a lot of activity!
Furthermore, every member of my Test Engineering & Architecture (TEA) team at Q2 completed four pull requests for Hacktoberfest, thus earning our prizes and our bragging rights. For the three others on the team, this was their first Hacktoberfest, and Boa Constrictor was their first open source project. We all joined together to make Boa Constrictor better for everyone. I’m very proud of each of them individually and of our team as a whole.
Personally, I gained more experience as an open source project maintainer. I brainstormed ideas with my team, assigned work to volunteers, and provided reviews for pull requests. I also had to handle slightly awkward situations, like politely turning down pull requests that could not be accepted. Thankfully, the project had very little spam, but we did have many potential contributors request to work on issues but then essentially disappear after being assigned. That made me appreciate the folks who did complete their pull requests even more.
Overall, Hacktoberfest 2021 was a great success for Boa Constrictor. We added several new features, docs, and quality-of-life improvements to the project. We also got people excited about open source contributions. Many thanks to Digital Ocean, Appwrite, Intel, and DeepSource for sponsoring Hacktoberfest 2021. Also, special thanks to Digital Ocean for featuring Boa Constrictor in their Hacktoberfest kickoff event. Keep on hacking!