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Die Entwicklung und Wartung von Software-Systemen wird ständig komplexer, da die entwickelte Software selbst immer komplexer und umfangreicher wird. Daher bietet sich zur Entlastung der Projektleiter, Projektmanager und weiterer Projektmitarbeiter eine Rechnerunterstützung der Software-Entwicklung und -wartung an. So können sie einen Überblick über den gesamten Prozess bekommen und diesen optimieren. Eine Möglichkeit der Unterstützung liefert die Modellierung des Software-Entwicklungsprozesses. Um einen Software-Entwicklungsprozess modellieren zu können, müssen die notwendigen Basisstrukturen identifiziert und bereitgestellt werden, was Thema dieser Arbeit ist.
Paris (Plan Abstraction and Refinement in an Integrated System) [4, 2] is a domain independent case-based planning system which allows the flexible reuse of planning cases by abstraction and refinement. This approach is mainly inspired by the observation that reuse of plans must not be restricted to a single description level. In domains with a high variation in the problems, the reuse of past solutions must be achieved at various levels of abstraction.
EADOCS (Expert Assisted Design of Composite Structures) is the implementation of a multi-level approach to conceptual design. Constraint-, case- and rule-based reasoning techniques are applied in different design phases to assemble and adapt designs at increasing levels of detail. This paper describes a strategic approach to decomposition, formulation of target design problems, and incremental retrieval and adaptation. Design problems considered, cannot be decomposed dynamically into tractable subproblems. Design cases are retrieved for requirements and preferences on both functionality and the solution. Cases are adapted in three phases: adaptation, modification and optimisation.
Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is to show that the same kind of results apply also for diagnosis. We propose a theoretical complexity analysis coupled with some experimental tests, intended to evaluate the adequacy of adaptation strategies which re-use the solutions of past diagnostic problems in order to build a solution to the problem to be solved. Results of such analysis show that, even if diagnosis re-use falls into the same complexity class of diagnosis generation (they are both NP-complete problems), practical advantages can be obtained by exploiting a hybrid architecture combining case-based and modelbased diagnostic problem solving in a unifying framework.