The increasing degree of parameterizability and resulting degree of software variability leads to new challenges regarding software development- and test processes. In order to be able to deal with an increasing software variability, different modeling approaches such as feature models are provided. For providing a personalized software variant to the customer, the resulting models are taken into account in build processes or during runtime. This increasing degree of software “mass customization” makes, for example, the search for relevant test cases more complex. A major goal of OpenSpace is the development of Machine Learning approaches that efficiently support different tasks related to software testing & debugging in order to be able to deal with the increasing complexity of software. In this context, OpenSpace develops variability-aware test methods which play a major role in the context of software product lines. From the viewpoint of research, OpenSpace will develop new approaches 1) to the automated analysis of variability models and corresponding test case generation and 2) to the machine learning based identification of faulty software components and faulty/suboptimal parametrizations which could lead to inefficiencies or erroneous behavior.