Please use this identifier to cite or link to this item:
Title: The Handbook of Engineering Self-Aware and Self-Expressive Systems
Authors: Chen, Tao
Faniyi, Funmilade
Bahsoon, Rami
Lewis, Peter R.
Yao, Xin
Minku, Leandro L.
Esterle, Lukas
First Published: 5-Sep-2014
Publisher: University of Paderborn on behalf of the EPiCS project consortium
Citation: The handbook of engineering self-aware and self-expressive systems, 2014.
Abstract: When faced with the task of designing and implementing a new self-aware and self-expressive computing system, researchers and practitioners need a set of guidelines on how to use the concepts and foundations developed in the Engineering Proprioception in Computing Systems (EPiCS) project. This report provides such guidelines on how to design self-aware and self-expressive computing systems in a principled way. We have documented different categories of self-awareness and self-expression level using architectural patterns. We have also documented common architectural primitives, their possible candidate techniques and attributes for architecting self-aware and self-expressive systems. Drawing on the knowledge obtained from the previous investigations, we proposed a pattern driven methodology for engineering self-aware and self-expressive systems to assist in utilising the patterns and primitives during design. The methodology contains detailed guidance to make decisions with respect to the possible design alternatives, providing a systematic way to build self-aware and self-expressive systems. Then, we qualitatively and quantitatively evaluated the methodology using two case studies. The results reveal that our pattern driven methodology covers the main aspects of engineering self-aware and self-expressive systems, and that the resulted systems perform significantly better than the non-self-aware systems.
Version: Publisher Version
Type: Report
Rights: Copyright © the authors, 2014. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License ( ), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Appears in Collections:Reports, Dept. of Computer Science

Files in This Item:
File Description SizeFormat 
1409.1793v3.pdfOther2.73 MBAdobe PDFView/Open

Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.