Since QF-Test 10, AI-driven capabilities are integrated directly into your testing workflow. This post shows how to connect GitHub Copilot to QF-Test using a simple custom model and the Copilot CLI.
Artificial Intelligence – or more exactly: the LLM – is a revolution for software development: never before has it been so easy and fast to produce software. However, LLMs come with a quality problem. They have a loose relationship to reality because they are “only” statistical models. They can generate output, but they cannot take responsibility for it.
This makes software QA more important than ever. We must ensure quality through human oversight while keeping pace with the sheer volume and speed of modern software development.
This is the balance QA must achieve – and it is our mission at QF-Test: to deliver AI integrations that boost the productivity of test automation engineers without compromising or calling into question the quality of their output. QF-Test is designed to build on the expertise of QA professionals, not to replace them.
QF-Test therefore takes a deliberate, transparent approach to Artificial Intelligence, applying it precisely where it delivers real, measurable value in day-to-day testing – without sacrificing QA teams’ control at any stage.
Next QF-Test Webinar
When tests become intelligent: AI-driven checks with QF-Test
On Monday, March 2, 2026, 3:30 PM – 4:30 PM CET, in English
In this special webinar, we’ll show you how to get the most out of the new AI integrations in QF-Test. With QF-Test 10, you can use AI to test non-deterministic UIs, validate UI components based on semantic criteria, generate test data, and much more.
Why AI matters in software testing today
Modern software is increasingly complex. Applications consist of numerous components, undergo continuous development, and need to work reliably across multiple platforms. At the same time, frequent changes to the user interface quickly destabilize classic, rule-based tests and drive up maintenance costs. Manual testing is time-consuming and can hardly scale as release frequency increases.
This is exactly where Artificial Intelligence in software testing comes in. QF-Test leverages AI specifically to make tests smarter, more adaptive, and more robust. Application changes can be handled more effectively, test runs become more stable, and feedback is provided faster – an essential advantage for agile teams and modern DevOps processes.
AI features in QF-Test
QF-Test applies Artificial Intelligence wherever it creates real added value in everyday testing – with the goal of making test automation more stable, efficient, and maintainable. The AI-powered features are integrated into key phases of the testing process, providing real relief for QA teams.
Bring your own model
QF-Test allows you to integrate most existing AI providers with just a few clicks. No additional costs and no additional maintenance effort.
Ask AI during test execution
Thanks to the
ai scripting module, you can easily send prompts to any AI configured in QF-Test. Use this to generate test data or perform natural language checks.
Check text with AI
With our fully-integrated “Check text with AI” node, you can validate components in your Applications UI using natural language prompts. This allows you to validate based on “fuzzy” criteria such as language or content.
More coming soon
We’re always working on adding new features to QF-Test, and AI is an especially important area for us.
All these features have a common goal: less maintenance, more stable tests, and faster decision-making. See for yourself how AI-driven test automation with QF-Test works in real-world scenarios.
Artificial Intelligence on our blog
Max Melzer
Martina Schmid
Best practices for AI test automation & AI testing with QF-Test
Successful implementation of AI in test automation requires a thoughtful approach that combines machine learning and proven testing strategies. AI should always be seen as support, not a replacement for QA experts – it complements expertise but does not take over strategic decisions. A gradual rollout with critical test cases lets you evaluate the benefits of AI, gain hands-on experience, and minimize risks. It’s also essential to ensure continuous monitoring and adjustment of AI outputs to guarantee reliability and adapt to changing requirements.
With these best practices, AI-powered test automation becomes a controlled, efficient, and sustainable part of your development process – optimally utilizing AI-driven testing, test intelligence, and autonomous testing in quality assurance.
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Limitations & challenges
Although AI can make test automation significantly more efficient and stable, there are important limits to be aware of.
- AI is a tool, not a person – it only supports existing processes and cannot substitute QA teams’ planning or expertise.
- AIs view of the world is based on statistics and training data and does not necessarily reflect reality. AIs can and will hallucinate and make stuff up.
- Management of false positives and false negatives: AI cannot always automatically classify every anomaly, so human review remains necessary.
QF-Test provides full transparency for all AI-driven decisions through the run log, allowing testers to see exactly how results are determined. This ensures that AI-driven test automation with QF-Test remains reliable, controllable, and practical.
Conclusion – QF-Test makes AI test automation practical
Business software requires reliability, not experiments. With QF-Test, AI test automation is efficient and transparent, used only where it offers real value. The result: fewer error-prone tests, faster feedback loops, and more time to focus on truly value-adding tasks.