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Model Quality
Metrics improve development process, understanding of complexity, discovering/predicting faults and estimation of efforts. Often the choice of metrics exceed what is required and therefore it is advised to define what is the purpose of measurement.
Framework for Deciding Metrics
A useful framework to derive useful metrics is the Goal-Question-Metric (GQM) paradigm developed by Basili [2]. The GQM process is as follows:
Define goals of measurement (for “UML As Sketch” type models then goal is mainly focusing on communication.)
Define questions that have answer that allow the observer to determine whether the goal has been met
Each question is evaluated to determine what metrics are required to support the question's answer.
Quality Goals
Mohagheghi [1] show that there are 6 classes of quality goals for models:
Comprehensibility (emphasis for model sketches)
Confinement (emphasis for model sketches)
Correctness (emphasis for model design)
Completeness (emphasis for model design)
Consistency (emphasis for model design)
Changeability (emphasis for model design)
Comprehensibility - Model is understandable to its intended audience.
Confinement - Model is in agreement with its purpose
Correctness - Model includes correct elements & relationships. Does not violate rules and conventions
Completeness - All necessary information is included at the necessary degree of fidelity
Consistency - Model is without contradictions. Horizontal consistency is consistency between diagrams and views that belong same level of abstraction. Vertical consistency is consistency between models or diagrams at different levels of consistency. Consistency also refers to semantics, i.e. same element does not have multiple interpretations
Changeability - Model supports continuous and rapid improvement and evolution
MIT xPRO propose the following “Qualities of Great Models”
Linked to Decision Making.
Model Credibility - it is believable
Clear Scope
Verification and Validation of Model - Model should show why it is the preferred option to do verification and validation of the system
Traceable and Analyzable
Understandable and Well Organized
Data extrapolation - where is the model valid
Complete relative to Scope
Internally consistent
Verifiable
Validatable
Elegant
Appropriate level of Fidelity
Allows Optimization - does it includes gradients or convexity
Avoid optimization on a black box
Reuse
Availability of Interfaces
Model Metrics
References
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[2] V.R. Basili, G. Caldiera, and H.D. Rombach, “The Goal Question Metric Paradigm”, In Encyclopedia of Software Engineering, volume 2, John Wiley and Sons, 1994, pp. 528-532.
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