. Reuse of software knowledge is a principle for improving productivity and reliability of software development. To achieve this, reuse must be done systematically. This means that processes for retrieving, reusing, revising, and retaining have to be defined. At the same time organizational issues (such as the establishment of a separate organizational unit responsible for organizational learning) must be considered. In this paper we compare software knowledge reuse models to the CBR cycle of Aamodt and Plaza [1] and show that the approaches are very similar. We suggest to extend the CBR cycle by including organizational issues explicitly and conclude that CBR is a promising technology for realizing software knowledge reuse if our suggested organizational extensions are considered. Keywords. Organizational View on CBR, Organizational Learning, Experience Factory, Quality Improvement Paradigm, Software Knowledge Reuse 1 Introduction Reuse practice appears to exhibit consid...

Case-based planning is the reuse of past successful plans in order to solve new planning problems. This paper presents a survey of case-based planning, in terms of its historical roots, underlying foundations, methods and techniques currently used, limitations, and future trends. Several authors have given overviews on case-based reasoning and specific topics such as case retrieval, case adaptation, and learning. This overview differs in focus. Its aim is to emphasize the case-based approach to planning, its methodological issues, and its relation to classical planning and the other kinds of case-based reasoning. It also provides some reference models. Keywords: Case-based planning, Case-based reasoning, Planning, Plan Retention, Plan Retrieval, Plan Reuse, Plan Revision. Statement of Exclusive Submission: This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to Artificial Intelligence Review. 1 Contents 1

The benefits of an organizational memory are ultimately determined by the usefulness of the organizational memory as perceived by its users. Therefore, an improvement of an organizational memory should be measured in the added perceived usefulness. Unfortunately, the perceived usefulness has many impact factors (e.g., the precision of the user query, the urgency with which the user needs information, the coverage of the underlying knowledge base, the quality of the schema used to store knowledge, and the quality of the implementation). Hence, it is difficult to identify good starting points for improvement. This paper presents the goal-oriented method OMI (Organizational Memory Improvement) for improving an organizational memory incrementally from the user's point of view. It has been developed through several case studies and consists of a general usage model, a set of indicators for improvement potential, and a cause-effect model. At each step of the general usage model of OMI, pro...

It is an important industrial need to deliver high-quality knowledge-based systems and organizational memories (e.g., to support service management or knowledge management in general). Evaluation is required to ensure this high quality and guide the development and maintenance. We present an approach for facilitating practical evaluation of organizational memories that meets the requirements for good measurements in knowledge engineering. The base of this methodology is the Goal-Question-Metric (GQM) technique, which is an industrial-strength technique for goaloriented measurement and evaluation from the field of software engineering. The practical benefit of GQM is demonstrated by a case study where GQM was applied to an existing case-based reasoning system/application.

We will introduce an infrastructure called Experience Factory that supports organizational learning in software development, i.e. the systematic reuse of all kinds of software knowledge. We are detailing existing software knowledge reuse process models using a knowledge level framework for case-based reasoning, based on an extension of the case-based reasoning cycle of Aamodt and Plaza [AP94]. Currently an experience base, the experience factory subpart where the knowledge is stored, is being built using case-based reasoning focusing on software inspections carried out in industrial software organizations.

We describe an approach for developing knowledge-based medical decision support systems based on the rather new technology of case-based reasoning. This work is based on the results of the Inreca European project and preliminary results from the Inreca+ project which particularly deals with medical applications. One goal was to start from case-based reasoning technology for technical diagnosis, as it was available among the partners, and ‘scale-up ’ to more general non-technical decision support tasks as typically given in medical domains. Inreca technology is used to build an initial decision support system at the Russian Toxicology Information and Advisory Center in Moscow for diagnosing poison cases that are caused by psychotropes.

. Reuse of software knowledge is a principle for improving productivity of software development and reliability of software systems. To achieve this, reuse must be done systematically. This means that methods for retrieving, adapting, and learning have to be defined. In this paper we present MIRACLE, a model which integrates the reuse of software knowledge and case-based reasoning. MIRACLE extends the CBR task-method decomposition model of Aamodt and Plaza [AP94] on one hand and details existing software knowledge reuse models on the other hand. Using general methods from case-based reasoning MIRACLE is operationalized, resulting in an operational model for learning from past experiences wrt. software development. In this sense MIRACLE can be regarded as a model for organizational learning. 1 Introduction The benefits of software reuse are manifold. Among them are increased productivity, higher reliability, better estimates, and shorter time-to-market [SPM94]. Traditionally,...

In the past many experience factory case studies and experiments have been carried out. We summarize some development steps and research results that, from our perspective, are important. We especially focus on the integration of experience factory and case-based reasoning and report on the respective benefits and impacts of such a seamless integration for building (more) autonomous and automated knowledge-based information systems, which will be of increasing importance in the future. It is our goal to build software-agent-enacted experience factories that improve case bases using maintenance and learning methods.

Recent work within the CBR community has studied more systematic means of interrelating different types of domain knowledge, tasks, and methods for building and maintaining CBR systems. This paper reviews work from the knowledge acquisition community, targeted at methodologies and tools for analysis, modeling, and maintenance of knowledge components. It is argued that the knowledge level is the appropriate level for describing the behavior of an intended CBR system, and for identifying the contents of its knowledge components. A framework is outlined in which a knowledge-level modeling methodology is adapted for the modeling and maintenance of CBR knowledge contents.

An increasing amount of work in AI in general, and case-based reasoning in particular, is addressing means of integrating different types of reasoning methods for building and maintaining AI systems. This paper first takes a step back in an attempt to identify a descriptive and comparative framework for the corresponding methods. It is argued that the knowledge level is the appropriate level for describing the behaviour of an intended system, and for identifying its methods and knowledge components. Main research activities in the author’s research group in Trondheim over the last twenty years are summarized with reference to the framework. 1.