As someone who transitioned from software development and data science into the world of quality assurance (QA), my first weeks at System Verification were a whirlwind of discovery. While my expertise in machine learning (ML) and MLOps was warmly welcomed, I quickly realized I had to learn more about QA and testing. It was a steep learning curve, but the eagerness of my colleagues for technical insights gave me a clear mission: to bridge the gap between our strong QA foundation and the rapidly evolving AI landscape.

In the ideal world of software development, releases should be moments of celebration. It’s the time when our hard work finally becomes a reality, delivered to the users. Yet, instead of joyous milestones, releases often become our greatest source of stress. We plan ceremonies around these releases, but the anxiety often overshadows any sense of accomplishment.  

In the thrilling race to utilize the power of generative AI, companies around the world are jumping on the bandwagon faster than you can say “artificial intelligence.” But, as the saying goes, "Not all that glitters is gold"—and not all AI projects are destined for glory. In fact, according to a recent article from Computer Sweden, nearly a third of these shiny new AI projects might end up in the digital dumpster. Ouch.

In our last article, we discussed how important it is to follow the most beneficial and crucial recommendations while developing or maintaining test automation solutions. The benefits are large and well worth the time and effort required to acquire them.

In this article, we will continue with recommendations. 

As previously said, there are numerous suggestions and practices that should be followed when developing AT solutions.

Most QA engineers today are already following the standard of implementing test automation solutions on their projects. But are we assessing our AT solution to ensure that we are getting the most out of it? Do we use AT recommendations and best practices, and how crucial is that? Is it even possible to apply best practices to every project? 

Let's find out together. 

Discover the journey to the ISTQB AI certification, from motivation to preparation tips. Gain insights on artificial intelligence (AI) testing, skills acquired, and advice for aspiring candidates.

In the rapidly developing world of technology, few terms have gained as much visibility as those related to Artificial Intelligence (AI). From machine learning to neural networks, these buzzwords dominate tech conversations, often leaving many feeling overwhelmed and confused. However, understanding these concepts is crucial, especially in domains like Software Quality Assurance (QA), where AI holds massive potential to revolutionize processes and outcomes.

Over the past two weeks, one of our consultants from the BiH office, Azure DevOps engineer Ahmed Babić, led an engaging on-site training program on Azure Pipelines, guiding participants from “zero to hero” level. It was a great opportunity and something we continuously strive for, to share knowledge among the teams, boosting our competency and the delivery of our projects. The training program included more than 20 participants, who attended all four organized sessions. The number of attendees ensured the training program remained engaging, sparking many discussions and prompting numerous questions.

We have thus far talked about quite concrete steps toward making quality a first-class citizen of the modern agile software development setting. Those will get us 80% there. But, as in any other process, the last 20% will require 80% of the total time, effort, and experience.