With the recent rise of machine learning and artificial intelligence, the explainability of software has found its way into the focus of research activities. Black box-like approaches that take critical decisions must be enabled to justify its actions in a comprehensible manner. This thesis takes these considerations and applies them to the area of operating systems and problem analysis thereof. To do so, a user study, conducted among professionals, is presented that shows that simplifying the generation of explanations of the operating system behavior can bring additional value. Furthermore, already available tools will be discussed based on their capabilities with regard to explanation generation. Subsequently, a new approach is proposed that allows to visualize decisions taken by the operating system in a decision graph. These graphs allow to examine how and why a certain value was set by the operating system in a convenient and efficient way. Finally, this approach is evaluated in another user study, which is again conducted among professionals. The final conclusion of this thesis then yields, that an increased focus on explainability capabilities in the context of operating system problem analysis would bring additional value to people working in this area. There is a wide range of other publications that focus on either problem analysis or explainable software, but not on the combination thereof. The proposed approach aims to connect the two areas by providing assistance in deriving explanations and justifications for the internal reasoning processes of operating systems in a convenient way. The potential value is successfully confirmed with an evaluation study conducted among professionals.