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Relating Dynamical Systems to
Software Engineering
“Some of the most exciting interactions between mathematics and engineering are occurring in the area of analysis and control of uncertain, multivariable, and nonlinear systems. While changing technology has made control and dynamical systems theory increasingly relevant to a much broader class of problems, the interdisciplinary nature of this area means that they no longer have a natural home exclusively or even primarily within any one of the traditional engineering disciplines.”
From CalTech’s DS&C Website
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Dynamical Systems Basics
• The change of state in a system over time• Autonomous & control systems• Continuous & discrete systems• Characterized by some level of complexity
– Degrees of freedom => # of dimensions– Incompleteness – Godelian
Dynamical Systems Basics
• Possible meta-states of outcomes– Deterministic– Vascillation– Chaotic
• Some combination of the above
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SE Basics
• Best practices• Formalized processes & procedures• Phases and iterations• Possible meta-state outcomes
– Deterministic (hopefully)– Vascillation– Chaotic
Decisions, decisions
• Is it important to be precise?– π = 3.14…– π = 3.141592653589792384626433832795…
(6.4 Billion* digits and counting)
• Is it important to be correct?– π = the constant ratio of the circumference of
a circle to the diameter*American
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Heisenberg Uncertaintyin the Software Engineering
SystemThe more precisely you know
where you are (your state),the less precisely you know where
you’re going (your transition)
Lorenz AttractorPhase shifting between statespace and transition space
Tools from MOF / UML World
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A Change of State …
Understanding
Confusion
Analysis ?
Over time
Requirement n
Epiphany*
*A sudden manifestation of the essence or meaning of something.
State
Transition
Start
Event
End
Outside Observer
…Can Become Complex
Understanding
Confusion
Epiphany*
Mealy
Moore
Time
*A sudden manifestation of the essence or meaning of something.
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Space(The Final Frontier?)
State SpaceTransition SpacePhase Space
Model SpacePaper Space
Defining Time & Space
• Iteration => Discrete time (ordered)• A State Space• A Transitional (Phase) Space
– In the ‘state’ of transition?– Transitional vector between
• State spaces• State space and a Phase Space• Phase Spaces => meta-Phase Space
Discrete
Discrete
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A State Space
Blue => confusionWhite => understanding
Blue => DocumentaryWhite => AnalysisYellow => NormalizationRed => SynthesisAqua => Realization
Embedding
Meaning of orderIn space
A Transition Space
State 1Confusion
State 2Understanding
PossiblePseudo-States?
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A Phase Space
AutomataRules for Transitions
• Rules can be expressed in mathematical, textual and graphical dimensions
• The iteration of rules can show behavior and meta-state evolution.
• Rules may be unidirectional or bi-directional => rules may be Hamiltonian or dissipative.
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Possible Conclusions and Derivative concepts
• Methods may be formalized using concepts of space, state, transition and phase to predict meta-state behavior in software engineering systems.
• Replicate the space concepts discussed to include a model space of all known informational dimensions – not just an abstraction.
• Paper space may be a projection of 2 dimensions of the model onto a traditional medium.
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Contact Information
Kenneth A. Lloyd, Jr.PresidentWatt Systems Technologies, Inc.3444 Briar PlaceCarmel, IN 46033 USA
Phone: 317-846-4088Email: [email protected]
Web: http://www.wattsys.com
The companies and individuals listed above hereby grant a royalty-free license to the Object ManagementGroup, Inc. (OMG) for worldwide distribution of this document or any derivative works thereof within OMG andto OMG members for evaluation purposes, so long as the OMG reproduces the copyright notices and the belowparagraphs on all distributed copies.
The information contained in this document is subject to change with notice.
UML is a trademark of the Object Management Group.
Copyright © 2002, Watt Systems Technologies, Inc.Copyright © 2002. Kenneth A. Lloyd, Jr.
All rights reserved.