Predictive Analytics to Give Quality Engineering a Facelift
Change is the only constant. While this applies to everyone in all walks of life, it applies the most in the agile technology world. Things change so rapidly—tools, technology, products, end users, competition, and teams—whether you're in a small or a large establishment. Quality engineering, which is a core function in the end to end app development process, is no exception. It is one of the most impacted areas of emerging technologies—and when I say most impacted, it is like clay. It's in the hands of the quality organizations, industry, and teams, and they determine if it's shaped into something useful or if it crumbles to pieces.
The evolutions around predictive analytics, artificial intelligence (AI), machine learning (ML), IoT, and augmented reality have a lot to offer to the discipline—and the good thing is they are not independent silos. They are largely related, while there are some differences too. If we look closer, what really is testing? It is a process of objectively validating a product under test to further "predict" how the product will function in the marketplace, including functional and non-functional elements. And if those predictions don’t seem good from your business, competition, and user expectations, what needs to be done to move into a state of release readiness is also an important area where quality fits in.
Why can't this be done by AI and ML models that learn what the expectations are and combine them with predictive analytics to bring in measurable outcomes? To some extent, automation does this today, but automation is still only as smart as we design it to be. Automation combined with AI and ML is what can enable predictive analytics to produce smart outcomes, and that is the facelift quality engineering will soon receive.
Does this mean all of testing will be taken over by these evolving technologies and the role of a tester will be nullified? Definitely not; one has to be smart to understand the role of testers among these new-age technologies. As the evolution cycle continues and just like testers moved from manual to automated testing to continuous testing, predictive analytics will be doing a lot of smart testing. Testers will continue to focus on building smart models, refining the models as required, and continuing to focus on elements that need human intervention that are difficult to train at a modal level. Similarly, specific areas of automation deep seated at the core system level may still be difficult to train through models. That said, even the more human-connected areas such as accessibility are slowly becoming trainable, including accessibility checks for alt tags, keyboard access, and collaborative filtering engines.
Stay tuned—a smart combination of predictive analytics and value-based automation that is lean is here to revolutionize the product industry at large.
