Devops and continuous testing trends and projections for 2018

What’s expected in next year in the mobile, cross-browser testing and devops landscape.

agile devops

As 2017 comes to a close, it’s time to get ready for what’s expected next year in the mobile, cross-browser testing, and devops landscape. Let’s look at the following trends:

  • Devops and test automation on steroids will become key for digital winners.
  • Artificial intelligence (AI) and machine learning tools alignment as part of smarter testing throughout the pipeline.
  • The internet of things (IoT) and digital transformation moving to prime time.

Devops and automation at scale

In 2017, we saw tremendous adoption of agile methods such as ATDD and BDD. As well as seeing organizations leaving legacy tools behind in favor of faster and more reliable and agile-ready testing tools. This move reflects the need to cater to every role that touches continuous testing such as dev, BA, test, or ops.

In 2018, we will see the above growing to a higher scale, where more manual and legacy tools skills are transforming into more modern ones. The growth in continuous testing (CT), continuous integration (CI), and devops will also translate into much shorter release cadence as a bridge towards real continuous delivery (CD).

Related to the above, to be ready for the devops and CT trend, engineers need to become more deeply familiar with tools like Espresso, XCUITest, Earl Grey, and Appium on the mobile front. As well as with open-source web-based frameworks like the headless Google project called Puppeteer, Protractor, and other web-driver-based framework. Additionally, teams will need to optimize the test automation suite to include more API and nonfunctional testing as the UX aspect becomes more and more important.

Shifting as many tests left and right is not a new trend, requirement, or buzz—nothing has changed in my mind around the importance of this practice. The more you can automate and cover earlier, the easier it will be for the entire team to overcome issues, regressions, and unexpected events that occur in the project life cycle.

Artificial intelligence, machine learning, and smarter test automation

In 2018, vendors will continue to see tools to optimize their test automation suite and shorten their overall execution time on the “right” platforms. The terms “AI” and “machine learning” (or “deep learning”) are still unclear to many tool vendors and are being used in varying perspectives that don’t always mean AI or machine learning.

The end goal of these solutions is very clear and the problem it aims to solve is real: Long testing cycles on mobile devices, desktop browsers, IoT devices, and more, generate massive amounts of data to analyze. As a result, it slows down the devops engine. To remediate this pain point in the new year, teams should use efficient mechanisms and tools that can crawl through the entire test code, understand which tests are the most valuable, and which platforms are the most critical to test due to either customer usage or history of issues.

Another angle or goal of such tools is to continuously provide a more reliable and faster test code generation. Coding takes time and requires skills that vary across platforms. To focus the team on pertinent goals rather than spending time on test code maintenance, it’s important to have a working machine learning/AI tool that can scan through the app during the testing phase and generate robust page object model (POM) and functional test code that runs on all platforms and respond to changes in the UI.

Additionally in 2018, organizations will look to leverage more consistent web design patterns to allow richer experiences. Google is pushing hard on a combination of web and native experiences as part of the website designs. We’ll see others follow suit by embedding mobile-native capabilities like GPS, camera, and others into websites using the progressive web design approach.

IoT and the digital transformation

In 2017, Google, Apple, Amazon, and other technology giants announced innovations around digital engagements. To name a few, better digital payments, digital TV, AR, and VR development API and new secure authentication through Apple’s FaceID. On the other hand, IoT hasn’t shown a huge leap forward. However, for specific verticals like health care and retail, IoT started serving a key role in their digital user engagements and digital strategy.

In 2018, I believe that the market will see an even more advanced wave in the overall digital landscape where Android TV and Apple TV, IoT devices, smartwatches, and other digital interfaces become the standard in the industry. This shift will require enterprises to rethink and rebuild their entire test lab to fit these new devices. It will also force test engineers to adapt to new platforms and rearchitect their test frameworks to support the multiscreen movement.

Bottom line

Although 2018 will bring change, don’t immediately change your current practices, but validate whether what you have right now is future-ready and can sustain what’s coming in the near future. If devops is already in practice in your organization, make sure you can scale, shorten release time, increase test and platform automation coverage, and optimize through smarter techniques your overall pipeline.

AI and machine learning buzz are really happening and will continue to grow in 2018. However, the market needs to properly define what it means to introduce these into the SDLC and what success looks like if developers do consider using them. From a landscape perspective, these tools are not yet mature and ready for prime time, so that leaves more time to properly prepare for them.

Copyright © 2017 IDG Communications, Inc.

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