A reflective framework for fine-grained adaptation of aspect-oriented compositions

Paul Grace*, Bert Lagaisse, Eddy Truyen, Wouter Joosen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

Dynamic Aspect Oriented Programming (AOP) technologies typically provide coarse-grained mechanisms for adapting aspects that cross-cut a system deployment; i.e. whole aspect modules can be added and removed at runtime. However, in this paper we demonstrate that adaptation of the finer-grained elements of individual aspect modules is required in highly dynamic systems and applications. We present AspectOpenCOM, a principled, reflection-based component framework that provides a meta object protocol capable of fine-grained adaptation of deployed aspects. We then evaluate this solution by eliciting a set of requirements for dynamic fine-grained adaptation from a series of case studies, and illustrate how the framework successfully meets these criteria. We also investigate the performance gains of fine-grained adaptation versus a coarse-grained approach.

Original languageEnglish
Title of host publicationSoftware Composition - 7th International Symposium, SC 2008, Proceedings
Pages215-230
Number of pages16
DOIs
Publication statusPublished - 7 Apr 2008
Event7th International Symposium on Software Composition, SC 2008 - Budapest, Hungary
Duration: 29 Mar 200830 Mar 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4954 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Symposium on Software Composition, SC 2008
CountryHungary
CityBudapest
Period29/03/0830/03/08

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    Grace, P., Lagaisse, B., Truyen, E., & Joosen, W. (2008). A reflective framework for fine-grained adaptation of aspect-oriented compositions. In Software Composition - 7th International Symposium, SC 2008, Proceedings (pp. 215-230). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4954 LNCS). https://doi.org/10.1007/978-3-540-78789-1_17