Grapefruit series - Harness AI in your Testing mindset

Let's first look at the startups and their Dev-centric approach. Cold facts, show that beside the core development, there are little (dedicated) gravitating roles: PM, SysAdmin, QA, Support.

In such a fast paced and agile environments, one can see future trends. For instance, how AI affects our daily IT jobs. As automation testers, at some point I guess we have all asked ourselves: "Am I automating myself out of a job?". I know I did automate an automation test engineer in the past. And the scary part is - it took just a couple of days, image recognition library and Selenium. The solution was imperfect and slow, but good enough for the business. She had to go. More and more IT tasks will be done by lines of code automatically written by a machine.

For a very long time, we're at our first stop on the way to AI: automation. Ask a veteran sysadmin what their daily life was like 10 years ago, and you will realize just how much automation has already happened in the field. Some things you might not even think of, simply because we now take them for granted. Need an example? In the "old days", for a website with medium complexity you needed a team, now a single resource (freelancer) can do a job that took a team of maybe three or four to do just a decade ago. The technologies require less and less system administrators to set them up, configure, maintain and scale them. You no longer need to take extra care of monitoring your environments or scaling of the system. It is done automatically according to expected usage (predictive provisioning).

Technology has been steadily impacting the jobs of project managers for years now, too. Just a few quick examples: offload truly routine tasks to increase value, coordinating tasks to increase efficiency and collect updates from the team in order to produce reports or raise triggers. Budget is a big part of the project, AI tools can now chalk out the most optimum and financially viable schedule and budget for any kind of project based on projection modeling techniques. Automatic project trackers (like Timely) are showcasing more and more benefits.

Support divisions and workflows also changed rapidly in the past years. I have witnessed first levels of the service desk blown away by chatbots and FAQ articles automation. The digital transformation took only a few weeks and there was no significant gap in the operations. The users got faster answers, reduced research and predictive insights for just a fraction of the old cost. The company focused beyond reaping first-contact efficiencies and invested in spotting patterns that will help the team perform root cause analysis to prevent issues.  

There are countless ways that Machine Learning can benefit a software tester. The thing is, one could not simply take the algorithms of AI and apply them to another game as-is (even though they are more general-purpose than in the past). As engineers, we should embrace this power and put in our human touch in the new service we provide. Advances in the AI field are exponential.
New challenges are rising, and the trick is to figure out what skills those challenges require. Here is one - utilisation of chatbots in software testing! How we can use the domain knowledge incorporated in them!? How to harvest the user interactions (expectations, questions) and convert them into test cases!?