Specification matching of software components
ACM Transactions on Software Engineering and Methodology (TOSEM)
Making Components Contract Aware
Computer
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Recognizing behavioral patterns atruntime using finite automata
Proceedings of the 2006 international workshop on Dynamic systems analysis
Dynamic Detection of COTS Component Incompatibility
IEEE Software
The Palladio component model for model-driven performance prediction
Journal of Systems and Software
Adaptive fuzzy-valued service selection
Proceedings of the 2010 ACM Symposium on Applied Computing
Reverse engineering with the reclipse tool suite
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2
The iSeM matchmaker: A flexible approach for adaptive hybrid semantic service selection
Web Semantics: Science, Services and Agents on the World Wide Web
Towards an automatic service discovery for UML-based rich service descriptions
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
A survey of fuzzy service matching approaches in the context of on-the-fly computing
Proceedings of the 16th International ACM Sigsoft symposium on Component-based software engineering
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In the future vision of software engineering, services from world-wide markets are composed automated in order to build custom-made systems. Supporting such scenarios requires an adequate service matching approach. Many existing approaches do not fulfill two key requirements of emerging concepts like On-The-Fly-Computing, namely (1) comprehensiveness, i.e., the consideration of different service views that cover not only functional properties, but also non-functional properties and (2) fuzzy matching, i.e., the ability to deliver gradual results in order to cope with a certain extent of uncertainty, incompleteness, and tolerance ranges. In this paper, I present a fuzzy matching process that distinguishes between different fuzziness sources and leverages fuzziness in different matching steps which consider several service views, e.g., behavior and quality properties.