**We defined a generic testing procedure that works across a diverse set of academic search systems** - all with distinct coverages, functionalities, and features. Thus, while other testing methods would be available, we chose the best common denominator across a heterogenic landscape of databases. This way, we can test a substantially greater number of databases compared to already existing database overviews. We test the functionalities of specific capabilities search systems have or claim to have. Here we follow a routine that is called **"metamorphic testing"**. It is a way of testing hard-to-test systems such as artificial intelligence, or databases. A group of researchers titled their 2020 IEEE article "_Metamorphic Testing: Testing the Untestable_". Using this logic, we test databases and systems that do not provide access to their systems. Metamorphic testing is always done from the perspective of the user. It investigates how well a system performs, not at some theoretical level, but in practice - how well can the user search with a system? Do the results add up? What are the limitations of certain functionalities?