What is a black box?

As a black box each system of the deployed artificial intelligence Designates whose inputs and operations are not visible to the user. In general, a black box is an impenetrable system.

At Deep Learning black-box development is usually performed. So the algorithm takes millions of data points, processes that input and correlates certain data features so it can produce an output. In the Data mining on the other hand, it is an algorithm or even a technology that cannot give any explanation for how it works.

A black-box model for developing software with artificial intelligence is an adequate development model for testing software components. This is not the case with search algorithms, Decision trees and knowledge-based systems that have been developed by AI experts, are transparent and offer comprehensible solution paths, from white-box processes.

A black box in the Machine learning is a model of a purely statistical nature. White-box models, on the other hand, denote analytical and physical descriptions for which modelling is often very elaborate. Finally, grey-box models combine both approaches and can unite the respective advantages.

What are typical methods?

A Black box testing is always used when there is no knowledge of the inner workings and implementation of the software. Only the outwardly visible behaviour is included in the test.

A successful test is not a sufficient indication of a successful and error-free system. Thus, a non-requested functionality or a massive security gap may remain undetected. Therefore, one test procedure is usually not sufficient, since structural tests cannot detect missing functionality and functional tests only insufficiently consider the existing implementation. The best approach is a combined procedure of functional testing with limit analysis or random testing, structural testing of the sections that were not covered and regression testing after error correction.

Functional tests can only insufficiently consider the implementation at hand. Test methods include functional tests (black box test) with a test case selection based on a specification. Thus, equivalence class tests are carried out, limit values are calculated and the test is narrowed down via special values. State tests can be implemented on this specification basis.