Drawing on both clients' evolving needs and technological advances in the market, we've identified the following top 5 trends that we see shaping Quality Assurance (QA) in 2025.

  1. Focus on accessibility
  2. Demand for multiskilled testers
  3. Growth in automated testing
  4. AI support in manual testing
  5. Continuous QA

Let's take a closer look...

1. Focus on Accessibility

New European regulations set to come into force this year will make accessibility a focus area for both enhanced testing on projects and audits of existing technology.

The European Accessibility Act will become law across the EU on June 28th 2025. It affects all companies trading digital products and services in the EU that have an EU-facing digital commerce operation. We are supporting more clients with accessibility audits and have IAAP WAS certified professionals ready to assist. We continue to evolve accessibility processes throughout the SDLC to ensure we offer compliant solutions.

We are utilizing multiple tools to support the change. For example, we are using Axe DevTool's extension for fast manual tests, plus the Axe-core and our internal a11y lab frameworks for automated checks. Additionally, we have multiple testers specialized in using screen readers, e.g., NVDA, and Voiceover.

2. Demand for multiskilled testers

In the new era of testing, where AI support is prevalent, demands on individual testers are increasing. Instead of focusing on one skill, the expectation is increasingly that people will have comb-shaped skill sets, with multiple areas of specialist knowledge arising from a single broad base.

Potential over-reliance on AI means that domain knowledge specialization is crucial. To effectively utilize information provided by LLMs – and to avoid AI hallucinations – you need to clearly understand your objectives. Only then can you leverage the vast amounts of information to improve your work.

Another aspect of being a multiskilled tester is adapting to the steady flow of new tools. You need to determine which tools are worth learning, which are useful for your project, and which are merely trending online. This year will see the introduction of completely new frameworks that will reshape testing, requiring an elastic approach to your toolset.

Always cited as one of the most important skills in QA, communication will become even more vital, along with a learning mindset that will improve your position in a team.

3. Automated testing will grow substantially

Solutions such as getting AI assistance with code writing through tools like Copilot are now industry standard. Using such tools to improve the quality of code and updated documentation is now part of daily operations for automation specialists. Tools like aide.dev and Windsurf are evolving rapidly to become even more powerful and support easier development.

We're also witnessing a new wave of low-code/no-code tools. Platforms like momentic.ai, octomind and QA Wolf may enhance your test process, making the entry threshold so low that most testers can utilize them. At VML, we continue to evaluate them for our use cases and data security requirements.

AI isn't the only trend in automation. Trends from previous quarters continue to evolve. For example, Playwright now has twice as many downloads as Cypress – indicating the market's preference for testing modern web applications. The popularity of Playwright has also been boosted by solutions like AutoPlaywright, API testing, and integration with Axe tools.

4. AI support in manual testing is now an obvious choice

AI support in manual testing is now a no-brainer – the integration of AI in multiple testing tools and the widespread availability of cost-effective text-based AI has brought substantial changes to manual testing. Multiple market analyses show that AI is being utilized for test planning, test case optimization, and test reporting. It also significantly improves the quality of bug reports.

For test data generation, testers will utilize various LLMs. While Anthropic’s Claude, Google Gemini or ChatGPT are preferred for text generation, other specialized solutions are better suited for image generation. In the future, we will see more widespread use of Expert AI solutions (such as Google Gems) for consulting on current solutions, as well as ‘thinking’ AI models. The growing general level of knowledge for when to use LLMs and which to use - based on your specific use case to get the best outcome alongside advanced prompting techniques - are allowing QAs to use these tools in a wider range of use cases, so the value continues to increase. At VML, our teams have access to multiple LLMs and training on how to use them.

These trends will be more prominent in less regulated industries. In highly regulated sectors like banking or medicine, QA engineers will still be needed to ensure accountability. We haven't yet reached the era where AI can be fully accountable in these sensitive domains.

5. Continuous QA

Continuous QA means implementing more quality assurance activities automatically and consistently throughout the development lifecycle. As automation shifts to leverage AI capabilities, and QA shifts left, the role of QA becomes more consultative than operational. With AI assistance, professionals can handle a broader scope of work – hence the need for multi-skilled individuals who include prompt engineering in their QA toolkit as standard.

The shift-left philosophy continues to trend, especially in security testing. Early scanning of code for vulnerabilities and automated scanning of projects in production will become standard practice. A proactive approach in these areas enables earlier defect detection and reduces the risk of data breaches.

The "automation first" mindset will become even more prevalent. To accelerate the SDLC, automation is essential for minimizing human error. This means the entire team must participate in various levels of testing, from unit testing to E2E testing. As always, process champions will be needed to establish governance and maintain standards.

Summary

To summarize, we see engineering practices adopting AI toolsets for faster delivery, allowing more QA to be automated and integrated earlier into the development process. With the power of AI agents and tools, the 10x QA has a breadth of skills and will cover multiple quality aspects, such as accessibility, performance, and security for example, and take a somewhat consultative role in a continuous QA project environment.

Emily Perowne BW

Emily Perowne

Director of Quality Assurance

Piotr Gackowski

Piotr Gackowski

Senior QA Engineer

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