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.

In the previous articles, we talked a lot about the increased technical demands on the Quality Assurance engineer when transitioning to a modern agile setting. These technical considerations are quite effective at paving the road, gaining access to all relevant forums. To reach the goal of becoming a first-class citizen, however, quality needs to be everywhere. This takes a lot of hard work and persistence. Team composition is also particularly important. Decent quality levels can only be achieved if the responsibility is shared.

When talking about transitioning from more traditional ways of testing, we tend to downplay the impact on the skills required from a Quality Assurance engineer. The fundamentals of test case design are, of course, the same. All the same methods applied when designing test cases, such as boundary value analysis, equivalence partitioning, etc., are still valid. However, the way of identifying the input to these methods is different. So is the implementation as automation is crucial. And this is where the technical demands often increase.

“Going Agile” is not just about organizing teams and learning Scrumish frameworks. Being Agile is about adopting to a specific mindset, enabling us to follow the constant and rapid flow of changes. Especially important tools supporting this mindset are continuous integration and continuous deployment. To be able to integrate with confidence and deploy with managed risks, having a quality focused approach is essential.

Looking back over the years, we can see that projects tend to use test efforts as Quality Insurance, rather than a part of the Quality Assurance efforts. Having well-designed test cases exercising relevant functionality from different perspectives are, of course, fundamental. If done right, it is probably the best way to determine if deliverables are designed to specification. This cannot, however, be viewed as an insurance policy put on the development process against any errors introduced along the way.