Implementing faceted classification for software reuse
Communications of the ACM - Special issue on software engineering
Case-based reasoning
Case-Based Reasoning in Design
Case-Based Reasoning in Design
Status Report: Software Reusability
IEEE Software
A Retrieval Method for Exploration of a Case Memory
EPIA '97 Proceedings of the 8th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Experiments On Adaptation-Guided Retrieval In Case-Based Design
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Similarity-driven software reuse
CVSM '09 Proceedings of the 2009 ICSE Workshop on Comparison and Versioning of Software Models
Reusable software components framework
ECS'10/ECCTD'10/ECCOM'10/ECCS'10 Proceedings of the European conference of systems, and European conference of circuits technology and devices, and European conference of communications, and European conference on Computer science
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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.