Authors: Caterina Rotondo
Complexity in technical development increases rapidly. Regular system are no longer able to fulfill all the requirements. Organic computing systems are inspired by how complexity is mastered in nature. This leads to a fundamental change in software engineering for complex systems. Based on machine learning techniques, a system develops self*-properties which allows it to make decisions at runtime and to operate with nearly no human interaction. Testing is a part of the software engineering process to ensure the functionality and the quality of a system. But when using self-organizing, adaptive systems traditional testing approaches reach their limits. Therefore, new methods for testing such systems have to be developed. There exist already a lot of different testing approaches. Most of them developed within a research group. Nevertheless, there is still a need for further discussion and action on this topic. In this paper the challenges for testing self-organizing, adaptive systems are specified. Three different testing approaches are reviewed in detail. Due to the ongoing fourth industrial revolution it is discussed which of these approaches would fit best for testing industrial manufacturing robots.
Comments: 6 Pages.
[v1] 2019-03-02 04:32:21
Unique-IP document downloads: 11 times
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.