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Advancing Semantics-Based Aspect Composition: Strategies and Future Directions

This paper explores Semantics-Based Aspect Composition, highlighting its potential to leverage the underlying semantics of languages in composition processes. We examine three key approaches, including Lancaster's AORE method, natural language semantics, and classification within domain-specific modeling languages (DSMLs). We address the sources of semantic information, distinguishing between explicitly given and derived semantics, and discuss the advantages of aspect decoupling. Future research avenues include balancing semantic information provision with the return on investment and examining concept relevancy within ontologies.

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Advancing Semantics-Based Aspect Composition: Strategies and Future Directions

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  1. Semantics-Based Aspect Composition

  2. Participants • Awais Rashid, Lancaster University, UK • Ruzanna Chichyan, Lancaster University, UK • Ana Moreira, New University of Lisbon, Portugal • Mansour Zand, University of Nebraska Omaha, USA • Jiang Ningkang, E China Normal University, Shanhai • Wu Yan, University of Nebraska Omaha, USA • Jon Whittle, George Mason University, USA

  3. What is Semantics-Based Aspect Composition? • Leveraging the semantics of the underlying language in composition • Defining a semantics for composition languages • Composition using the semantics of the domain of the application We focused on (3)

  4. Approaches Discussed • Lancaster approach for AORE • Natural language semantics (e.g., verb categories, synonym lists) • Whittle/Araujo/Moreira • Classification of concepts • Domain-specific modeling languages (DSMLs) • Comes with semantics of concepts but may be limited to specific domains

  5. Where does the semantics come from? • Semantic information usually not present • Must be explicitly given or derived • Explicitly given: • Annotations/DSMLs/etc. • Derived: • Alpha (TU Darmstadt) • From natural language semantics

  6. Advantages • Decouples the aspect: don’t refer to name or number that may change • Could potentially avoid many of the consistency problems associated with composition

  7. Avenues for Future Research • Trade off of providing the semantic information versus the ROI in using it • Cf.static analyzers • Relevancy: given an ontology, how can I quantify how relevant or related concepts are to each other? Chair->session chair->table. Could possibly use context? • Claim: semantics-based architecture composition less well understood than requirements • DSMLs: • Semantics usually implicit in code generation tools • Domain-specific composition strategies

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