As we about to wrap out 2017, It’s the right time to get ready to what’s expected next year in the mobile, cross-browser testing and DevOps landscape.

To categorize this post, I will divide the trends into the following buckets (there may be few more points, but I believe the below are the most significant ones)

  • DevOps and Test Automation on Steroids Will Become Key for Digital Winners
  • Artificial Intelligence (AI) and Machine Learning (ML)/ Tools alignment as part of Smarter Testing throughout the pipeline
  • IOT and Digital Transformation Moving to Prime Time


DevOps and Automation on Steroids

If in 2017, we’ve seen the tremendous adoption of more agile methods, ATDD, BDD and organizations leaving legacy tools behind in favor of faster and more reliable and agile-ready testing tools, such that can fit the entire continuous testing efforts whether they’re done by 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, and with the open-source web-based framework like the headless google project called Puppeteer, Protractor, and other web driver based framework.

In addition, optimizing the test automation suite to include more API and Non-Functional 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 change 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.

AI, ML, and Smarter Test Automation

While many vendors are seeking for tools that can optimize their test automation suite, and shorten their overall execution time on the “right” platforms, the 2 terms of AI and ML (or Deep learning) are still unclear to many tool vendors, and are being used in varying perspectives that not always mean AI or ML 🙂

The end goal of such solutions is very clear, and the problem it aims to solve is real –> long testing cycles on plenty of mobile devices, desktop browsers, IOT devices and more, generates a lot of data to analyze and as a result, it slows down the DevOps engine. Efficient mechanism and tools that can crawl through the entire test code, understand which tests are the most valuable ones, and which platforms are the most critical to test on due to either customer usage or history of issues etc. can clearly address such pain.

Another angle or goal of such tools is to continuously provide a more reliable and faster test code generation. Coding takes time, requires skills, and varies across platforms. Having a “working” ML/AI tool that can scan through the app under test and generate robust page object model, and functional test code that runs on all platforms, as well as “responds” to changes in the UI, can really speed up TTM for many organization and focus the teams on the important SDLC activities in opposed to forcing Dev and Test to spend precious time on test code maintenance.

IOT and The Digital Transformation

In 2017, Google, Apple, Amazon and other technology giants announced few innovations around digital engagements. To name a few, better digital payments, better digital TV, AR and VR development API and new secure authentication through Face ID. IOT this year, hasn’t shown a huge leap forward, however, what I did notice, was that for specific verticals like Healthcare, 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 and Apple TV, IOT devices, Smart Watches and other digital interfaces becoming more standard in the industry, requiring enterprises to re-think and re-build their entire test lab to fit these new devices.

Such trend will also force the test engineers to adapt to the new platforms and re-architect their test frameworks to support more of these screens either in 1 script of several.

Some insights on testing IOT specifically in the healthcare vertical were recently presented by my colleague Amir Rozenberg – recommend to review the slides below 


Bottom Line

Do not immediately change whatever you do today, but validate whether what you have right now is future ready and can sustain what’s coming in the near future as mentioned above.

If DevOps is already in practice in your organization, fine – make sure you can scale DevOps, shorten release time, increase test and platform automation coverage, and optimize through smarter techniques your overall pipeline.

AI and ML buzz are really happening, however, the market needs to properly define what it means to introduce these into the SDLC, and what would success look like if they do consider leveraging such. From a landscape perspective, these tools are not yet mature and ready for prime time, so that leaves more time to properly get ready for them.

Happy New 2018 to My Followers.