State of AI applied to Quality Engineering 2021-22

Executive Introduction


Authors of this page
Mark Buenen, Sogeti
Antoine Aymer, Sogeti

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AI is becoming essential for Quality Engineering

Over the last years AI[1] (Artificial Intelligence) has become a reality for most of us. Self-driving cars, facial recognition, chatbots, and deep-fake image production are only a few of the commonplace examples of today. The advances in computing power combined with storage cost efficiency is driving drive more of AI developments at increased pace.

Parallel to the growth of AI, the software development industry has been profoundly impacted by the increase in technological sophistication, the reduction of delivery cycle time, and the imperative of the user experience. And the number of potential combinations of digital products has now reached unfathomable proportions. All these competing forces create a chasm in terms of quality engineering. Many of us have turned to Continuous Testing to help close this gap. However, looking forward, we realize this is becoming insufficient.



[1] Put simply, AI is the ability of machines to carry out tasks and activities we would consider "intelligent". Artificial intelligence, broadly defined, is the ability for an intelligent agent to observe its surroundings and carry out specific tasks to maximize its ability to achieve some goal. Source:

The advancement of artificial intelligence raises two intriguing questions about our discipline:

  1. How do we use artificial intelligence to make quality validation smarter?
  2. How do we easily and effectively validate AI solutions?

This report aims to assist you in understanding the potential of AI and how it can help improve the quality, velocity, and efficiency of your quality engineering activities. In partnership with leading technology providers, we provide specific suggestions, ideas, and examples that can help you address the first question. We will return to you with answers to the second question in a subsequent series.

We will dive into the different quality assurance practices, illustrating each with specific use cases:

  • Section 1 delves into the fundamentals of the QE and AI convergence,
  • Section 2 discusses how artificial intelligence can be used to address some of the challenges associated with test design,
  • Section 3 looks at ways to improve the decision-making process,
  • Section 4 examines how to further automate functional testing,
  • Section 5 investigates approaches to GUI testing using computer vision,
  • Section 6 reviews how AI can be used to address test data challenges,
  • Section 7 addresses the role of AI in performance engineering,
  • Section 8 focuses on how artificial intelligence can improve security testing,
  • Section 9 discusses the role of AI in enhancing IT Operations
  • Section 10 examines how AI and ethics will continue to influence QE

We wish you an excellent read.

About the authors

(of the executive summary)

Antoine Aymer

Antoine Aymer

Antoine Aymer is a passionate technologist with a structured passion for innovation. He is currently the Chief Technology Officer for Sogeti's quality engineering business. Antoine is accountable for bringing solutions and services to the global market, which includes analyzing market trends, evaluating innovation, defining the scope of services and tools, and advising customers and delivery teams. Apart from numerous industry reports, such as the Continuous Testing Reports, the 2020 state of Performance Engineering, Antoine co-authored the "Mobile Analytics Playbook," which aims to assist practitioners in improving the quality, velocity, and efficiency of their mobile applications through the integration of analytics and testing.

Mark Buenen

Mark Buenen

Mark Buenen has more than 20 years international experience in software testing within Sogeti Group. Currently Mark is responsible for service innovation and growth of the testing portfolio at Capgemini and Sogeti Group.

Mark is a C-level advisor on transformational QA approach fitting with agile and DevOps methodologies. Mark has supported many of the large testing deals for Sogeti and Capgemini Group across the world. Mark has led the set-up of shared risk and outcome based commercial models for managed testing services. Mark is the lead-author of the yearly World Quality Report since 2013.

In his experience the establishment of a fit-for-purpose, flexible and effective test organization is possible for everybody. The key areas of such a successful solution for today and tomorrow are: 1. Quality integration in Agile and DevOps projects; 2. zero-defect development through shift left of quality processes; 3. zero-touch testing powered by intelligent and cognitive test automation solutions; 4. Continuous real time quality monitoring to manage outcomes and performance.

About Sogeti

Part of the Capgemini Group, Sogeti operates in more than 100 locations globally. Working closely with clients and partners to take full advantage of the opportunities of technology, Sogeti combines agility and speed of implementation to tailor innovative future-focused solutions in Digital Assurance and Testing, Cloud and Cybersecurity, all fueled by AI and automation. With its hands-on ‘value in the making’ approach and passion for technology, Sogeti helps organizations implement their digital journeys at speed.

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Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of 270,000 team members in nearly 50 countries. With its strong 50 year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group reported in 2020 global revenues of €16 billion.
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