Title
A Context-Role Based Modeling Framework for Engineering Adaptive Software Systems
Abstract
To engineer adaptive software systems, it is crucial to capture changes that drive adaptation. We believe that a pair of concepts, context and roles, is most effective in capturing such changes and creating an adaptation mechanism to deal with those changes. We propose a new framework for developing adaptive software systems based on Epsilon, a context-role oriented approach. The framework covers the whole process of adaptive system development, starting from the requirements phase through the implementation phase of program generation by means of model building and model transformation. We show effectiveness of our approach with a case study of the Traffic Jam Monitoring System. In this case, traffic jam occurrence, growth and dissolution are captured as context creation, merging and splitting and roles are played by intelligent cameras. How an Epsilon model is built and then successively transformed into a final program through several steps of model transformation is explained.
Year
DOI
Venue
2014
10.1109/APSEC.2014.25
APSEC (1)
Keywords
Field
DocType
implementation phase,model building,requirements phase,context-role based modeling framework,adaptation mechanism,role,context,engineering adaptive software systems,intelligent cameras,adaptive system development,traffic jam occurrence,model transformation,adaptive system,ubiquitous computing,traffic jam growth,traffic jam monitoring system,traffic jam dissolution,object-oriented programming,context-role oriented approach,epsilon model,context merging,context creation,context splitting,program generation,formal verification,context modeling,unified modeling language,java,adaptive systems
Model transformation,Unified Modeling Language,Systems engineering,Adaptive system,Computer science,Model building,Real-time computing,Context model,Adaptive software,Merge (version control),Java
Conference
Volume
ISSN
Citations 
1
1530-1362
4
PageRank 
References 
Authors
0.71
17
2
Name
Order
Citations
PageRank
Tetsuo Tamai133433.27
Supasit Monpratarnchai271.85