Usage-centric Testing part 2: a shift-right approach

The Dev/Ops infinite loop

In the first part of this article, we discussed the problems that come with End-to-End tests as our applications grow: longer feedback loop, irrelevant tests, maintenance complexity, and so on. In this second part, we will see how shift-right testing, and particularly “Usage-centric Testing”, can help overcome these problems.

Testing in the era of continuous deployment

The Dev/Ops infinite loop

DevOps practices accelerated the pace of software delivery dramatically. Some teams can push new improvements multiple times a day in production, so their users can see their tool evolve almost instantly. 

One of the enablers of such a speed can be summarized as a single mantra: “Test early, test often”. The ability to ensure quality continuously during all the phases of development is one of the foundations that makes DevOps possible.

Since we can’t spend days exploratory testing every new release candidate, we rely heavily on automated checks at every stage of the development. By having early feedback, the team can quickly fix the problems that may arise before releasing anything, which is far less expensive. 

In this will of getting feedback as often and early as possible, the QA effort has been spreading before and after the development itself. This is what is commonly known as shift-left (coined by Larry Smith in 2001, see the original article) and shift-right.

Shift-left groups all testing activities that happen in the phases of the “Dev” part of the DevOps infinite loop. QA is involved since the early stages of the cycle so that defects can be detected and fixed from the requirements definition itself. TDD (Test-Driven Development) is an example of what shift-left testing is (we write tests before coding). BDD (Behaviour-Driven Development), where user stories are discussed with the team, and their acceptance criteria are formalized as test scenarios and automated before any line of code is written is one another.

Shift-right, on the other hand, encompasses all the tests we can perform on our application in its production environment. Here again, we’re aiming to get feedback to continuously improve the quality of our product:

  • Detect production issues as early as possible. Nothing is closer to the production environment than the production itself. Switching from a controlled environment (development or staging) to “real life” can not be as smooth as expected. By watching the production environment, we can spot issues before they become critical for too many users and fix them before they degrade their experience. For example, many tools on the market allow teams to detect performance issues so they can quickly improve them. Techniques like canary testing can help check the integration of a new piece of code in the production environment by opening it to only a small subset of users.
  • Get insights into user preferences. The application in the hands of end-users is the only place where you can get reliable data about their habits and preferences. This is why practices like A/B testing have been democratized. Getting feedback from our product users helps us improve their usability and prioritize further developments. This feedback can also be used as input to better target our testing effort…


Learning from our users’ behavior to optimize our E2E tests

We can benefit a lot from seeking feedback from our application in its production environment. The idea of what we call here, at Gravity, Usage-centric Testing is to leverage users’ behavioral data to improve our test suite and shape it so we can find the perfect trade-off between velocity and reliability.

As we saw in the first part of this article, designing the most suitable End-to-End test suite is more complicated than it looks. We think that informing our testing decisions with what we know about our users’ behavior can greatly help:

  • Improve test coverage. Our tests are based on assumptions about how our users will interact with a feature. But as we know, they are not going to act as we expected, which means they might bump into regressions we weren’t able to detect so far. Learning from the production what are their most common journeys when using our applications makes us able to secure them with new regression tests, so we ensure these paths never break… and that we keep our users (and ourselves!) happy
  • Clean up End-to-End test suite. Learning how our features are used is also very valuable. It is the perfect opportunity to redesign (or even get rid of) End-to-End tests that are checking paths that our users never take. By being data-informed, we can focus our test effort on actual behaviors. Therefore, we benefit from both shortening our feedback loop (fewer End-to-End tests mean faster test runs) and protecting us against regressions on critical paths to our users.
  • Prioritize environments. Demographic data can also be leveraged. Devices we use to interact with software multiplied up, making testing even harsher. There are a lot of products on the market making cross-device testing easier (BrowserStack or TestComplete for instance). By getting insights into our users’ most common environments, we can effectively prioritize the test runs for these environments and, again, shorten our feedback loop. 

Here are a few examples of how we can benefit from helping our testing decisions with knowledge of the way our users are doing with our products. Now, let’s see how we can concretely do this.

How do we do that?

There are a lot of techniques and tools that can help us learn how our product is used.

  • Usability tests. These tests are a powerful tool in the UX designer toolbox. In short, you provide a task to be done with your application to a potential user, and you just observe how they work it out. They are a treasure trove of learnings about how people are dealing with your application. These tests are primarily designed to help improve the usability of your service, but they also can help design new and more accurate automated E2E tests
  • Web analytics. Unlike usability tests (that are expensive to set up), web analytics tools make it easy to collect product usage quantitative data. With tools like Google Analytics, Amplitude, or MixPanel, you can easily monitor user actions (clicks, business actions, page visits, etc…) and collect demographic information. Products like Hotjar go even further by providing behavior analytics features, like heatmaps or session recordings.
  • Gravity. All the tools we talked about so far are great and indeed help to understand users’ behavior. Their only flaw (in the context of our topic) is that they are not designed to assist with software testing. It is why we build Gravity, which offers functionalities like auto-detection of patterns in users’ behavior, test coverage against product usage, or Cypress script generation, to help you tailor a test suite fitted to your application’s actual usage.

We hope this article has given you some tips to help you answer this tricky question of “what to test?”We build software for humans. It is now common to adopt a user-centered design approach to create useful, desirable, and delightful services. As we saw, such an approach can also be beneficial for the efficiency of our delivery process, and to prevent regressions before they impact our customers’ satisfaction.

It is precisely what we are aiming for by developing Gravity as a Usage-centric Testing platform. Do not hesitate to contact us to learn more, or give it a try. We, too, want to deliver the best service possible, and your help would be really helpful in this journey!

Thank you for reading!

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