The structure-mapping engine: algorithm and examples
Artificial Intelligence
Concept learning and heuristic classification in weak-theory domains
Artificial Intelligence
Integration of domain analysis and analogical approach for software reuse
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
An approach to the classification of domain models in support of analogical reuse
SSR '95 Proceedings of the 1995 Symposium on Software reusability
Software reuse myths revisited
ICSE '94 Proceedings of the 16th international conference on Software engineering
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Building systems from commerical components
Building systems from commerical components
Case-Based Learning: Beyond Classification of Feature Vectors
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Technology Transfer for Reuse: A Management Model and Process Improvement Framework
ICRE '98 Proceedings of the 3rd International Conference on Requirements Engineering: Putting Requirements Engineering to Practice
ISPW '96 Proceedings of the 10th International Software Process Workshop
A process model of cased-based reasoning in problem solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
A two-stage framework for UML specification matching
Information and Software Technology
Similarity-Based Retrieval With Structure-Sensitive Sparse Binary Distributed Representations
Computational Intelligence
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Software reuse means to use again software components built successfully for previous projects. To be successful, techniques for reuse should be incorporated into the development environment. This paper presents an approach where analogical reasoning is used to identify potentially reusable analysis models. A prototype implementation with focus on the repository and analogical reasoning mechanism is presented. All models in the repository are described in terms of their structure. Semantic similarity among models is found by identifying distance in a semantic net built on WordNet, an electronic, lexical database. During retrieval of potential analogies, information about structure and semantics of models is used. During mapping, genetic algorithms are used to optimize the mapping between two models based on their structure and semantics. Experiments are described in which analogies are identified from the models in the repository. The results reported show that this approach is viable.