The verification and validation of software components are based on extensive testing. The required test cases to enable testing are derived from the specified requirements, which are then executed, and the results are compared with the acceptance criteria of the…
Using Machine Learning components in critical systems requires a sound safety concept and the ability to argue and prove that the risk of the considered system is acceptably low. In our previous blog post (Dealing with uncertainties of Machine Learning…
The use of Machine Learning (ML) components in safety-critical or financially critical systems is challenging. At Fraunhofer IESE, we address this challenge by systematically engineering comprehensive multi-layered safety concepts and explicitly considering sources of uncertainties. This specifically includes situations at…
