5x improvement in system development speed over current practice In order to do this metrics need to be used to synthesize and select the best possible system architecture A key element is to choose system architectures and designs that achieve desired performance ID: 782281
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Main goal of META is to achieve a 5x improvement in system development speed over current practice. In order to do this metrics need to be used to synthesize and select the best possible system architecture:A key element is to choose system architectures and designs that achieve desired performance with minimum necessary structural and dynamic complexity as well as maximum adaptability.Structural Complexity is based on the type and number of system elements, their interconnections as well as the structural arrangement. Captures both component an interface complexityKey concept: Graph EnergyDynamic Complexity is based on the number and correlation amongst functional performance attributes of the system.Captures correlation and uncertaintyKey concept: Shannon EntropyOrganizational Complexity captures the number of staff required and reworkPredicts META Program Cost and ScheduleKey Concept: The Rework Cycle (System Dynamics)Adaptability – the ease with which changes can be made to a design in the future
META Metrics Toolbox for quantifying complexity and adaptability of system designs
Conclusion: META-speedup factor of 5 compared to current practice appears feasible, but will require multiple “mechanisms” to work together (C2M2L library coverage > 0.8, structural complexity reduction of 40% thanks to extensive architecture enumeration, 75% probability of early detection of design flaws, >2 layers of abstraction etc…). The current META metrics toolbox produced by the UTRC team contains structural, dynamic and organizational metrics of complexity.
Contact Dr. Olivier de Weck (
deweck@mit.edu
) or Dr.
B
rian Murray (
MurrayBT@utrc.utc.com
) for more information on this effort.
Spectrum of System Metrics
Structural
Sample Results for GTF:
# of components: 73
# of interfaces: 377
Graph Energy: 124.8
Structural Complexity: 717.7
Metrics
Toolbox
Organizational
(Design Flow)
Dynamical
Structural DSM
Sample Results for B777 EPS:
Schedule completion time:
Actual 61 m, META: 16 m
ECRs
: 300, META: 100
META Speedup Factor: 3.8
Structural Complexity drives the amount of
Design and Integration Work required as well
As the Engineering Change Generation Rate
META System Dynamics Model
Sample Results for 3-spool Turbofan:
Dynamic Complexity: 708.2
Functional Interdependency E(B) = 34.5
Shannon Entropy = 20.5
Other dynamic complexity metrics
B – Matrix captures
correlations amongst
Functional attributes
19x19
Dynamic Complexity drives the amount of
change and number of iterations required to achieve satisfactory performance in all
Functional requirements
Sensitivity Analysis of META outcomes
Sample Results:
dimensional_complexity
20
computational_complexity
= 24.9869nonlinear_complexity = 7.7852timescale_complexity = 185.5963
It does not seem possible to compress META design evaluation into a single scalar metric.A vector of metrics is most useful, containing:Structural Complexity (parts, interfaces, architecture, Graph Energy)Dynamic Complexity (functional interdependence, uncertainty, Shannon Entropy)Organizational Complexity ( number and productivity of staff, organizational assignments, Schedule, NRE)Main Purpose of META Metrics Architecture SelectionOutcome prediction
META Metrics Takeaway