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.

A useful framework to derive useful metrics is the Goal-Question-Metric (GQM) paradigm developed by Basili [2]. The GQM process is as follows:

1. Define goals of measurement (for “UML As Sketch” type models then goal is mainly focusing on communication.)
2. Define questions to which the answers allow the observer to determine whether the goal has been met
3. Each question is evaluated to determine what metrics are required to support the question's answer.

Krogstie et al (referenced in [4]) is a framework which lays out that quality goals are defined as relations between blocks.

• Syntatic quality - goal that all statements in the model are according to the syntax of the modeling language
• Empirical quality - comprises comprehensibility matters such as layout and readability
• Organizational quality - model fulfills the goal of modeling
• Social quality - agreement between stakeholder's intepretations

No known empirical evaluation of this framework on models.

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 fidelity. 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

• Rationale includes:
  • Comparison of models (before and after, model A and model B on same system)
  • Measure of progress
  • Prediction of work effort
• Examples:
  • Absolute size (e.g. number of elements)
  • Relative size (e.g. ratio between sequence diagrams and use cases)
  • Complexity (…)
  • Functionality (e.g. number of use cases)
  • Re-use (e.g. applicable if a profile is used, then the metric may be % of profile usage)

• Degree of polymorphism
• Degree of information hiding
• Degree of complexity
  • Dynamicity / Coupling - complexity of a class internal behavior based on message calls and state transitions
  • Depth of Inheritance Tree (DIT)
  • Cohesion - which part of class is needed to perform a single task
  • Number of Use Cases - number of use cases per class
• Comprehensibility Metrics
• Number of elements on diagram
• Number of crossing lines on diagram
• Number of entry and exit points on diagram

• [1] P. Mohagheghi et al, "Existing Model Metrics and Relations to Model Quality" Accessed on Jul. 4th, 2020
• [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.
• [3] C.F.J. Lange, "Model Size Matters", Proc. Model Size Metrics Workshop, 2006.
• [4] P. Mohagheghi and J. Aagedal, "Evaluating Quality in Model-Driven Engineering" Access on Jul. 2nd, 2020