This paper describes an application of artificial intelligence to support software reuse. We begin by discussing the characteristics of software engineering that establish dynamic reorganization as a requirement for a repository of software artifacts. We then present an experimental system that uses incremental concept formation as the basis for dynamic reorganization, and the conceptual hierarchy that was generated by the system for a set of 67 artifacts. The hierarchy is compared to a hierarchy produced manually by independent investigators, and the automatic hierarchy is evaluated in terms of retrieval efficiency and retrieval reliability. The paper ends with a discussion of three projects that share similar objectives with our work.

When the idea of software reuse appeared in 1968, new horizons for software design were open. But some major problems appeared and most of the expectations were not met. One of the problems encountered is the selection of the right software component. This is related not only to the similarity between the desired functionality and the function delivered by the retrieved software component, but also to the effort needed to modify the chosen component to accommodate the desired functionality. Most of the research done in the case-based reasoning area has been in developing accurate and efficient retrieval algorithms. We think that case-based reasoning retrieval concepts and ideas can be successfully applied to software reuse. In this article we propose a metric to assess similarity between software cases supported on functional and behavioral knowledge. One important aspect of this metric is that reusability is taken into account to estimate the amount of effort needed to reuse retrieved software cases. We also present experimental work that shows that similarity at the functional level is the most important aspect of the similarity metric proposed.