Complementing detailed and complete diagnosis concepts like MBD, in recent years spectrum-based fault localization (SFL) \cite{abreu09Barinel} has been gaining in attention. With SFL we evaluate execution data about which component was involved in this or that observed behavior. The result is a ranking how suspicious the individual components are to have caused the failing behaviors. Traditionally, SFL has been employed for software debugging, but, e.g., it was shown recently in \cite{PW18sfl} how to translate the idea for a static diagnosis of knowledge-bases used in automated reasoning. As will be discussed in the next section, there we observed which of the knowledge-base's rules were involved in the individual reasoning tasks and whether the tasks were successful. Because of the ease of implementing SFL and the smaller number of information required for diagnosis, SFL presents a representative of light-weighted diagnosis methodologies.