A particular strength of case-based reasoning (CBR) over most other methods is its inherent combination of problem solving with sustained learning through problem solving experience. This is therefore a particularly important topic of study, and an issue that has now become mature enough to be addressed in a more systematic way. To enable such an analysis of problem solving and learning, we have initiated work towards the development of an analytic framework for studying CBR methods. It provides an explicit ontology of basic CBR task types, domain characterisations, and types of problem solving and learning methods. Further, it incorporates within this framework a methodology for combining a knowledge-level, top-down analysis with a bottom-up, case-driven one. In this article, we present the underlying view and the basic approach being taken, the main components of the framework and accompanying methodology, examples of studies recently done and how they relate to the framework. 1.

We introduce an organizational interpretation of the well-known case-based reasoning cycle of Aamodt and Plaza which is helpful for comparing the case-based reasoning approach to other approaches known from Software Engineering like the quality improvement paradigm and experience factory approach. We will point out that the introduction of an organizational view to the case-based reasoning cycle contributes to the modeling of and the decision support for experience guided business processes and we believe that this is a first step into the right direction for case-based reasoning to become a key technology for supporting organizational learning. In addition, even for simple application scenarios the applicability potential of casebased reasoning technology is made more explicit by including organizational issues that are usually necessary within real-life environments. 1. Introduction An increasing number of activities and publications underlines that the fields of Software and Knowle...