Currently when it comes to testing most people differentiate only manual and automated testing, however with the constant growth of new technologies, new testing techniques emerge.
We live in the age of data, and with that we are granted constantly with new tech, famous AI (Artificial Intelligence) and ML (Machine Learning) also start to appear in the world of software testing.
The bitterness of poor quality remains long after the sweetness of low price is forgotten - Benjamin Franklin
That quote just shows how the complex testing process, and why more and more is invested into R&D of testing processes and software quality.
In the near future we can expect our two friends — AI & ML to take care of the most of the work today’s software engineers do (yes, test automation engineer is a type of a software engineer 😃).
Their roles will be focused more on creating vast and beautiful experiences versus typical software problems and outcomes. In testing automation more effort will be put into creating different complex assertions rather than doing repetitive boring tasks.
Machine learning, as we know it today, is basically built on principle of constant testing, predictable models are being build from patterns and are tested and adjusted using test (training) data. So it’s no surprise that it is helping more and more in the area of Software testing.
Today we already have some of these tools available, code-less automation tools that only require user to use “drag-n-drop” new functionalities, inputting assertions into dialog fields — whole “code” is being written automatically.
Applications capable of that are even able to “self-heal” — this means that application is constantly learning. When a developer changes something and tests fail AI combined with ML will try to fix it by itself basing on the changes that have been done previously.
So, for instance if an ID changes on one of the input fields from
#telephone -> #contact_number
software will assume by association and previous experience that this field is in fact related to phone number and is the correct one.
It will try to pass the test with that assumption — if test passes (assertions are positive) this field will be flagged and suggested to the developer to be used for future runs.
Some companies even go as far as letting AI analyse their builds before deployment, using complex scoring systems and ML to nominate “winner” (best build). This build is then deployed and further analysed to improve machine learning algorithms.
AI also is helpful in reporting test results, it can analyse results and provide not only the result (pass or fail) but also tell why and when error happens, maybe an error only occurs on staging environment and not on production. These reporting tools can decrease the time needed to solve the issue. In future test engineers’ tasks will shift more to the analytical and monitoring side of things rather than to writing a plain code.
Also, maybe most importantly, AI can distribute available computing power to run tests in parallel greatly reducing the time needed to complete the test runs.
Bright future awaits security testers as well — ML software using historical data and well-trained models will be able to suggest (maybe even implement) changes to the application under test and provide near real-time protection.
Testers nowadays should definitely have some basic knowledge of selenium (test automation framework) — no matter the programming knowledge, more importantly testers should focus on:
Testers with skills listed above will surely be ready for the future, will deliver quality software, as well as provide income to the company they work for.
Worry not though, there is still a lot of work to be done in order to fully automate testing and there is a huge market for automated testers.
QA is one of the hardest markets to fully automate — after all a human has to check if it is something usable by humans.
One thing is for sure — there is a bright and interesting future ahead of us, testers.
If you are looking for test automation experts, that look further and are hungry for more, contact us — firstname.lastname@example.org
What are you most excited to see in the future? Maybe you have your own predictions?
ASO is the acronym for App Store Optimisation, which consist on many tactics and actions that can be done in order to better position a mobile application in app stores like Google Play, Apple Store and others. ...
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