AI-assisted software testing
Artificial intelligence is fundamentally changing software testing: test cases are automatically generated from requirements, test scripts repair themselves, and regression tests run in a fraction of the time they used to take. For companies in regulated industries, this means shorter release cycles with consistently high quality and full auditability.
sepp.med combines these new possibilities with over 40 years of experience in safety-critical systems. We use AI where it creates real added value and keep humans in the loop where compliance and critical judgment are required.
Your project deserves the best!
The benefits of AI-assisted testing
The benefits of AI-assisted testing
AI-assisted testing relieves your team of repetitive tasks and creates space for value-adding work.
✅ Faster test cycles:
You receive feedback faster and can release more frequently.
✅ Less maintenance:
Less manual maintenance thanks to self-healing mechanisms for test scripts.
✅ Higher test coverage:
AI detects gaps in coverage and generates targeted test cases for edge cases.
✅ Auditable and traceable:
All AI-supported test decisions remain transparent and documented.
✅ Human expertise remains central:
AI takes over routine tasks. Your testers have more time for exploratory testing, risk analysis, and complex business logic.
Typical challenges – and how we solve them
“Our test automation requires a lot of maintenance.”
✅ Our solution:
We implement self-healing tests that automatically adapt to changes. This significantly reduces maintenance effort and keeps your regression tests stable.
“We don't know which AI tools suit our requirements.”
✅ Our solution:
We evaluate tools such as Tricentis Tosca, Katalon, Diffblue Cover, and Applitools in the context of your industry and toolchain. You receive an informed recommendation on what really suits your use case.
“AI results must be auditable.”
✅ Our solution:
For regulated industries, we ensure that all AI-supported test decisions are documented in a traceable manner, in accordance with IEC 62304, ISO 26262, or GAMP 5.
“Our team has no AI experience.”
✅ Our solution:
We train your employees in prompt engineering, context engineering, and tool operation, as well as in the validation of AI-generated tests. This ensures that the expertise remains in-house.
“We need fast results, but no vendor lock-in.”
✅ Our solution:
Our approach is tool-agnostic. We integrate AI capabilities into your existing CI/CD pipeline—with tools that you can continue to operate yourself.
How sepp.med supports you
We analyze your existing test processes, toolchains, and data quality. You receive a well-founded assessment of which AI use cases will bring the greatest benefits in your context, including a roadmap and quick-wins.
Start with a limited pilot: We implement AI-supported tests for a critical area of your software and measure the effect on test time, maintenance effort, and error detection rate.
From Selenium and Playwright to Robot Framework and proprietary solutions, we enhance your existing automation with AI capabilities such as self-healing, intelligent prioritization, and automatic test case generation.
For companies looking to scale testing: Our test factory combines human expertise with AI-powered automation for fast, reliable, and compliant test results.
We prepare your team for AI in testing: from the basics (prompt engineering, tool selection) to advanced topics (validation of AI-generated tests, regulatory requirements).
AI knowledge provided by the sepp.med Academy
AI solutions on the sepp.med blog
FAQ on AI-assisted software testing
No. AI takes over repetitive tasks such as test case generation, script maintenance, and result triage. Human testers remain indispensable for exploratory testing, risk analysis, and complex decisions. In our projects, AI and humans work as a team with a clear division of roles.
AI-generated tests achieve a hit rate of 60 to 90 percent, depending on the context. That's why we rely on human-in-the-loop: critical test cases are always validated by experienced testers. For regulated industries, we document this review process in an auditable manner.
We work in a tool-agnostic manner and select tools based on requirements: for unit testing, for example, Diffblue Cover (Java) or Qodo; for UI testing, Katalon, Tricentis Tosca, or Applitools; and for API testing, Postman with AI extensions. The final tool recommendation is based on your tech stack and regulatory requirements.
A typical pilot project usually runs for around 8 to 12 weeks. Full integration into existing processes takes 6 to 18 months, depending on the scope. We recommend a phased approach: first quick wins (e.g., self-healing, visual regression), then expansion.
