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Quality engineering is about team members and their stakeholders taking joint responsibility to deliver IT systems with the right quality at the right moment. This involves all lifecycle activities. Some people think quality engineering is just a fancy name for testing, but it is much more. In this module you will read about a wide range of quality engineering activities and how GenAI can perform these activities as one of the crossfunctional team members.
People in a cross-functional team assume multiple roles to perform a wide range of tasks. This way, the team is flexible. Team members share the responsibility to complete all tasks on time.In this book, we use the term quality engineer to refer to individuals who are part of a cross-functional team and capable of performing various tasks. The term does not denote a specific job title but serves as an umbrella term for those involved in delivering the right level of quality at the right time.Quality engineering needs to be amplified by applying GenAI, other tools and smart approaches to ensure that every task within the IT delivery lifecycle is carried out in a balanced manner,concerning speed, quality, and business value. In today’s fast-moving world, the quality engineering conundrum of what to do and where to focus is magnified multifold. The convergence of physical with cyber has added another layer of complexity to quality engineering. The product strategy is shifting from building clearly defined products to building connectable eco-systems. Connections between such eco-systems have opened new opportunities as well as new vulnerabilities.
Amplified quality engineering is used to achieve an accelerated and optimized level of quality of the IT delivery process by using an intelligent approach to all activities in the software developmentlife cycle.This module gives insight into how GenAI can support the quality engineering activities to maintain the right level of quality and still increase the delivery speed.This module contains many new insights, building on top of the work that was done by the authors of the TMAP book Testing in the digital age: AI makes the difference [Ven 2018].
In a world where systems are deeply interconnected and subject to continuous change, the challenge remains to uphold quality standards while delivering business value at the right moment. An Agile mindset and DevOps culture have brought us far in terms of speed and adaptability. However, the stakeholder demand for acceleration persists. The next step lies in expanding automation—not only in operational tasks like test execution, but also in knowledge-intensive activities. Emerging practices such as pair programming with GenAI coding assistants illustrate how intelligent automation can support teams beyond traditional boundaries. These new possibilities enable IT delivery teams to reach the next level of speed in the evolution of the cycle time of quality engineering and testing.
The following building blocks are relevant within this module, Quality Engineering with GenAI and Agentic AI
This figure illustrates the increasing speed at which teams perform tasks. It’s important to remember that speed is not the only measure of success. Maintaining a balance between quality and time is essential, as described in the definition of quality engineering that says “the right quality at the right moment”.
GenAI, imitating intelligent human behavior, can help us get to this new level of quality at speed. To meet the challenges presented by accelerating delivery speed with increasing technicalcomplexity, we need to follow a very simple imperative: Work smarter, not harder!GenAI tools are very good at performing specific tasks to reach a certain goal. But it is still the human insight and creativity that determines the goal(s).The role of humans in the IT delivery process shifts further away from pure operational execution tasks towards elaborating vision and goals.Where once it was sufficient to define a vision and goals at intervals and then focus on execution, the emergence of GenAI has fundamentally changed the cadence. Development now occursso rapidly that strategic thinking can no longer be a static, occasional activity. Instead, it must become a continuous process, constantly evolving to guide and keep pace with the capabilitiesof intelligent automation.
The goal of quality engineering is to continuously deliver IT systems with the right level of quality at the right time. As the pace of IT delivery accelerates, the window for gathering informationabout the quality and related risks has narrowed. In the past, human testers often relied on personal expertise and gut feeling to assess quality. Today, we aim to augment that process withmodern GenAI-powered tools. However, these tools lack human intuition, so we must rely on other means. This is where traditional practices, such as test design techniques, become increasinglyrelevant to ensure that quality levels remain measurable, traceable, and actionable in this new era.
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