Mapping features to aspects: a model-based generative approach

  • Authors:

  • Uirá Kulesza;Vander Alves;Alessandro Garcia;Alberto Costa Neto;Elder Cirilo;Carlos J. P. De Lucena;Paulo Borba

  • Affiliations:

  • PUC-Rio, Computer Science Department, Rio de Janeiro, Brazil and New University of Lisboa, FCT, Computer Science Department, Lisboa, Portugal;Informatics Center, Federal University of Pernambuco, Recife, Brazil and Lancaster University, Computing Department, Lancaster, United Kingdom;Informatics Center, Federal University of Pernambuco, Recife, Brazil;New University of Lisboa, FCT, Computer Science Department, Lisboa, Portugal;PUC-Rio, Computer Science Department, Rio de Janeiro, Brazil;PUC-Rio, Computer Science Department, Rio de Janeiro, Brazil;New University of Lisboa, FCT, Computer Science Department, Lisboa, Portugal

  • Venue:
  • Proceedings of the 10th international conference on Early aspects: current challenges and future directions
  • Year:
  • 2007

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Abstract

Handling the various derivations of an aspect-oriented software family architecture can be a daunting and costly task if explicit support is not systematically provided throughout early and late development stages. Aspect-oriented software development (AOSD) has been recently explored as a technique that enables software product line customization. However, the application of AOSD has been limited to modularize specific crosscutting features encountered in the implementation of software product-line architectures or frameworks. Only a few works have investigated the development of product derivation approaches for AOSD. This paper presents a model-based generative approach to mapping features to aspects across different artifacts of a product line. Our main aim is to enable the smooth and systematic derivation of aspect-oriented software family architecture. Our approach is complementary to a set of previously-proposed modularization guidelines to implement aspect-oriented frameworks. We present details about the suite of mappings supported by our generative model, illustrate them in heterogeneous case studies, and discuss several implementation issues for its accomplishment.