Introduction
Quality assurance and software testing are facing a phase of profound change – in terms of organization, technology and personnel. While approaches such as DevOps, continuous testing and AI-supported automation are increasingly shaping everyday life in development projects, new requirements for role profiles, collaboration and technical skills are also coming into focus.
But what exactly can test managers, project managers and quality assurance experts expect in the coming years? In order to provide a well-founded answer to this question, I have collected numerous voices from the field – including from interviews, expert discussions and OpenSpace sessions. The findings from the ASQF’s “Quality Brunch” in Hamburg in early 2025 and the QS Barcamp in September 2024 were particularly useful. The discussions, experience reports and future scenarios conducted there form the basis for this article.
The focus is on three central topics in which the future of testing is emerging:
- Work environment and organizational structures
- (Soft) skills for testers
- Technologies in the test context
I systematically examine three questions for each of these topics:
- What will change?
- What remains the same?
- What is uncertain?
This structured approach provides test managers with a sound basis for preparing strategic decisions on organizational structure, training and technology deployment in a timely and future-proof manner.
Work environment and organizational structures
2.1 What will change
The classic model of independent test departments is becoming increasingly less important. In modern development organizations, interdisciplinary, often agile teams assume responsibility for quality throughout the entire development process. The “shift left” principle has become established: quality assurance starts early and is an integral part of product development.
In such structures, the nature of collaboration is also changing. Teams work more internationally, more frequently remotely and in faster cycles. English becomes the basis for communication – not only for meetings, but also for documentation, training and shared tools. The focus is no longer on the centrality of the function, but on its contribution to value creation and delivery capability.
With the increasing use of continuous integration and DevOps practices, traditional task and role models are also changing. Hierarchically organized leadership is becoming less relevant. Instead, skills for self-organization, mediation and consulting in flat, dynamic teams are gaining in importance.
2.2 What remains the same
Despite the growing speed of projects and increasing variety of tools, certain framework conditions remain stable. In regulated industries – such as medical technology, aviation or automotive – established standards and processes continue to apply. Standards such as IEC 62304, ISO 26262 or DO-178C require structured procedures, complete documentation and traceable traceability management.
Some basic principles also remain in place at a structural level: Technical expertise will continue to be organized on a topic-specific basis, for example in the test architecture, test data management or formal review processes.
Last but not least, critical questioning – as a core competence of quality assurance – retains its central importance. Even in highly automated environments, human judgment remains essential.
2.3 What is uncertain
It is unclear what role traditional forms of test management will play in the future. If agile teams increasingly organize themselves, the question arises as to whether central test responsibility is still necessary at all – or whether it will be perceived and distributed differently. The tension between technical and functional management in testing has also not yet been clearly resolved.
It is also uncertain how the significance of manual tests in the working environment will change. They may remain relevant in areas with a high need for exploration, but in many industrial processes they are under constant pressure to be automated.
(Soft) skills for testers
3.1 What will change
The scope of skills for testers is expanding significantly. Traditional testing and documentation skills alone are no longer enough. Comprehensive skills in the use of automation tools, scripting, test data management and continuous integration are required. If you want to remain successful as a tester, you need an understanding of code, architecture and systems.
At the same time, the need for communication skills is growing. As testers are increasingly involved in interdisciplinary work, they need skills in the areas of moderation, translation of technical content, conflict resolution and priority management.
Soft skills such as flexibility, problem-solving orientation and self-organization are becoming decisive criteria for success – especially in frequently changing technical and organizational settings.
3.2 What remains the same
The methodological principles of testing, such as equivalence classes, limit value analysis or condition-based test designs, remain relevant. Even in the age of automation, tests still need to be planned, analyzed and evaluated. Tools provide support – but not the conceptual work.
The role of testers as mediators in the team is also retained. They bring requirements, risks and systems together – and ensure a shared understanding of quality. In this role, they act as multipliers and transparent authorities in the project or product context.
3.3 What is uncertain
It has not yet been conclusively clarified what prospects there will be for testers with a purely manual focus. One thing is certain: The demand for purely executive roles is decreasing. However, whether this will result in new specializations (e.g. in the area of usability or ethics assessment) remains to be seen.
The further development of traditional career paths is also uncertain. Roles such as test managers without a technical foundation could be replaced by new profiles – such as quality engineers with an advisory function or product owners with responsibility for testing.
Finally, the growing use of AI in the testing process places new, previously non-standardized demands on qualifications and the distribution of responsibilities.
Technologies in the test context
4.1 What will change
Technological progress is fundamentally changing testing. Artificial intelligence and machine learning are already supporting the automatic creation, prioritization and analysis of test cases. These systems use historical data and current usage patterns to focus testing efforts on high-risk areas.
Embedded systems are increasingly incorporating AI components that have to be tested and audited themselves. This requires new test approaches, for example for explainability or behavior under unforeseen inputs (fuzzy testing).
There will be further consolidation on the tools side: Platforms for combined test management, infrastructure control and results analysis will prevail – ideally integrated into DevOps processes.
Another key issue is cyber security. Increasing networking via the cloud, IoT and mobile endpoints increases the attack surface. Security tests, including penetration testing and code hardening, are essential – as is the assessment of new vulnerabilities from supply chain risks or third-party components.
Post-quantum cryptography (PQC) is also becoming increasingly relevant. With a view to future quantum computers, new cryptographic processes must be integrated into systems and tested. New requirements for testers will also arise here – for example in performance tests or the secure migration of existing architectures.
Last but not least, digital twins and simulated test environments enable early testing under realistic conditions – a development that is becoming increasingly important, particularly in the embedded and systems sector.
4.2 What remains the same
Some technologies remain due to regulatory requirements or proven efficiency – these include structured test documentation, regression tests, load tests and review methods. Test management systems also retain their central role when it comes to traceability and controllability.
In safety-critical industries, established procedures will continue to apply in the future, as these will remain necessary as the basis for audits and certifications.
4.3 What is uncertain
It is still unclear to what extent AI can or should make real test decisions in the future. While tools can already prepare assessments or make automated recommendations today, the question of responsibility, traceability and legal protection for automated test assessment remains open.
It is also unclear how the testing of generative AI and autonomous systems (e.g. in vehicles or medical robotics) will develop methodically. There is currently a lack of standards for the evaluation of complex, dynamic decision-making logic.
Sustainability (keyword “green testing”) is also gaining in importance, but concrete technologies and processes for energy-efficient test design are still in their infancy.
In addition, there is uncertainty about how new attack vectors will develop – particularly in the course of the increasing softwareization of classic hardware products, the growing number of endpoints in the IoT context or autonomous systems with cloud connectivity. Test managers will have to be prepared to increasingly deal with dynamic threat scenarios and flexibly develop test strategies.
The bottom line is this: Technology is becoming a key factor influencing testing – both as an opportunity for automation and increased efficiency and as a source of growing complexity and risks. Anyone who wants to test in a future-oriented manner must not only apply technologies, but also critically analyze and continuously evaluate them – and question them when in doubt.
Conclusion
The next few years will be decisive for the future role of quality assurance in digital product management. Organizations that develop test strategies to fit, rely on adaptive structures and proactively accompany technological change will be able to identify quality risks more quickly and manage them more sustainably.
I don’t see testing as an obsolete model, but as a key discipline – albeit in a new form, with a changed skills profile and under changed responsibility models. Those who set the structural course today will also be able to take responsibility for digital products with high quality and safety in the future.
Or, to quote a well-known emperor with a slight wink: “Progress cannot be stopped – but it can be shaped.”
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