An Empirical Investigation of System Changes to Frame Links between Design Decisions and Ilities

Maintaining system performance in the presence of uncertainties in design and operating environments is both challenging and increasingly essential as system lifetimes grow longer. In response to perturbations brought on by these uncertainties, such as disturbances, context shifts, and shifting stakeholder needs, systems can continue to deliver value by being either robust or changeable. These lifecycle properties, sometimes called “ilities”, have been proposed as means to achieve system value sustainment in spite of changes in contexts or needs. Intentionally designing for these lifecycle properties is an active area of research, and no consensus has formed regarding how these and other “ilities” might trade off. This paper describes ongoing research that investigates empirical examples of system changes in order to characterize these changes and to develop a categorization scheme for framing and clarifying design approaches for proactively creating ilities in a system. Example categories from the data for system changes include: the perturbation trigger for the change, the type of agent executing the system change, and the valid lifecycle phase for execution. In providing a structured means to identify system change characteristics, this paper informs future research by framing possible relationships between ilities and design choices that enable them.

[1]  C. Eden BookOn systems analysis : David Berlinski 186 pages, £ 10.25 (Cambridge, Mass, and London, MIT Press, 1976)☆ , 1978 .

[2]  Adam Michael Ross,et al.  Managing unarticulated value : changeability in multi-attribute tradespace exploration , 2006 .

[3]  Adam M. Ross,et al.  11.1.1 Using Natural Value-Centric Time Scales for Conceptualizing System Timelines through Epoch-Era Analysis , 2008 .

[4]  Adam M. Ross,et al.  System architecture pliability and trading operations in tradespace exploration , 2011, 2011 IEEE International Systems Conference.

[5]  Adam M. Ross,et al.  Developing Methods to Design for Evolvability: Research Approach and Preliminary Design Principles , 2011 .

[6]  Guojun Gan,et al.  Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability) , 2007 .

[7]  Adam M. Ross,et al.  A methodological comparison of Monte Carlo Simulation and Epoch-Era Analysis for tradespace exploration in an uncertain environment , 2010, 2010 IEEE International Systems Conference.

[8]  Daniel E. Hastings,et al.  Defining changeability: Reconciling flexibility, adaptability, scalability, modifiability, and robustness for maintaining system lifecycle value , 2008 .

[9]  Daniel E. Hastings,et al.  Defining changeability: Reconciling flexibility, adaptability, scalability, modifiability, and robustness for maintaining system lifecycle value , 2008, Syst. Eng..

[10]  Charles S. Wasson System Analysis, Design, and Development: Concepts, Principles, and Practices (Wiley Series in Systems Engineering and Management) , 2005 .

[11]  James R. Wertz,et al.  Space Mission Analysis and Design , 1992 .

[12]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[13]  D.H. Rhodes,et al.  Empirical Validation of Design Principles for Survivable System Architecture , 2008, 2008 2nd Annual IEEE Systems Conference.

[14]  Yi Li,et al.  COOLCAT: an entropy-based algorithm for categorical clustering , 2002, CIKM '02.

[15]  Daniel E. Hastings,et al.  3.1.2 Two Empirical Tests of Design Principles for Survivable System Architecture , 2008 .