This dissertation investigates the explainability competence of case-based reasoning systems and enriches developers of CBR applications with a flexible extension of their already existing CBR applications by an additional component, which enables existing CBR applications to explain, how they reached their diagnosis result. For doing so, an approach presented by Jean-Baptiste Lamy et al. [83] has been implemented in Java and has been successfully tested in the domain of “Intrusion Detection Systems” as well as the domain of “Blood Alcohol Level Prediction”. The extension has been developed in the context of the myCBR framework for the development of Java-based CBR applications and, thus, been published publicly using an open-source license. A user study with 38 participants showed a good acceptance rate – independent of the participants’ experiences on artificial intelligence or the general stance towards artificial intelligence.