12. Dec 2025

Testing with artificial intelligence

Our test community is passionate about testing of all kinds. It is particularly important to them that tests are carried out sustainably. In this series, they share their best practices from many years of project experience. Today with Philipp Scheider.
1060 x 710 Weber Maike

Author

Meike Weber

202509 Nachhaltiges Testen

Artificial intelligence has long since found its way into the field of software testing, from automated test case creation to intelligent error detection. AI-powered tools can recognize patterns in code changes, dynamically adapt tests, and even identify potential vulnerabilities before a test is first run. The result: faster test cycles, earlier error detection, and more efficient quality assurance.

Quote from Philipp Schneider. Use AI tools for test automation and error detection. However, consider challenges such as high-quality data and a solid understanding of AI concepts to realize the full potential of AI in software testing.

But to fully exploit this potential, more than just the use of modern tools is needed. The basis for reliable AI-supported testing is high-quality, representative data. Poor or incomplete data sets lead to inaccurate predictions and undermine confidence in the results. Equally important is a solid understanding of the underlying AI concepts: only those who understand how the algorithms work can make targeted use of their strengths and circumvent their weaknesses.

Used correctly, AI becomes a powerful partner. It takes on repetitive, time-consuming tasks and gives the team the opportunity to focus on creative and complex test cases.

So, those who integrate AI into the testing process while investing in data quality and expertise lay the foundation for a new level of testing efficiency and raise software quality to a whole new level.