Telling the end-to-end story of AI in Quality Engineering
Back in 2012, I considered how to bring together all the providers of the (new at that time) mobile app testing market to produce an analytical industry report
It was a hugely ambitious plan and one that has since evolved into what I believe is now a project of far more substantive value than simply an analytics-based report.
I’m talking about a comprehensive real-world view of Artificial Intelligence (AI) and its use cases in software testing and quality assurance today. And I’m thrilled that my early ambitions have resulted in the release of the initial chapters of Sogeti’s State of AI applied to Quality Engineering 2021-2022. This is not so much a report or a white paper, but rather a detailed reference document that I hope people will come back to time and time again.
Whether you are an IT leader, software developer, or involved in any aspect of software quality engineering, this informed and informative document has been written and produced to offer insight that is easy to consume, in the way you want to consume it.
Introducing the first chapters
That’s why we’re releasing it in stages — culminating in the complete work later in 2022 — and offering an audio version alongside the written chapters. The discipline of Quality Engineering, as set out in Sogeti’s TMap® approach, is huge. A single article about the application of AI in this area could not possibly cover it all. At the same time, no single technology vendor or solution provider has all the use cases for AI in QE within their body of knowledge and project deliverables.
For these reasons, State of AI applied to Quality Engineering 2021-2022 is a convergence of industry best practices and concrete examples of AI’s application in QE. These come from quality assurance and testing specialists across Sogeti and the wider Capgemini Group, along with experts at leading technology providers that have deployed AI in their product to solve a specific QE challenge.
Each core QE activity is addressed in a specific section. And, similar to a TV series, each of the 10 sections (or seasons) and its chapters (or episodes) will be published on a nearly bi-weekly basis.
Don’t wait — it’s here right now
Our intention is to advise and guide the market in its AI decisions — not for tomorrow, but right now. So, we have asked our contributors to offer concrete proof of where their AI technology is an enabler for QE. What is AI being used for? What challenges does it address — are they new or existing ones that AI can solve faster? What can we learn from the current deployments? What QE activity should we prioritize? What are the ethical concerns influencing AI for QE choices?
These are among the many questions we set out to answer in State of AI applied to Quality Engineering 2021-2022. We have structured the document in such a way that it is easy for readers to identify a topic of interest and simply dip into that chapter. My hope is that the concrete evidence in this piece of work aids a broader realization that AI must be factored in when improvements to QE are being made.
I hope that it triggers conversations, both internally within development teams and externally with those experts able to take you forward on your Quality Engineering journey.
Enjoy the read as much as I have enjoyed seeing this long-term ambition come to fruition.
Read the Executive Introduction of State of AI applied to Quality Engineering 2021-2022