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.
My first chance to give back
When I graduated college, I was zealous for open source software. I believed in it. I was an ardent supporter. But, I was mostly a consumer. As a Software Engineer in Test, I used many major test tools and frameworks: JUnit, TestNG, Cucumber, NUnit, xUnit.net, SpecFlow, pytest, Jasmine, Mocha, Selenium WebDriver, RestSharp, Rest Assured – the list goes on and on. As a Python developer, I used many modules and frameworks in the Python ecosystem like Django, Flask, and requests.
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.