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bok:eng:mbse:intro [2020/07/05 09:18]
anwlur [Performance Qualities] added more metrics
bok:eng:mbse:intro [2020/09/11 12:19] (current)
anwlur [Parts] fixed internal link
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 ===== Performance Qualities ===== ===== Performance Qualities =====
  
-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.+See this page on [[bok:​eng:​mbse:​quality|model quality]].
  
-==== Framework for Deciding Metrics ====+See this page on how to [[bok:​eng:​mbse:​critique|critique a model]].
  
-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) 
- 
-{{ :​bok:​eng:​mbse:​intro:​mbse_intro1.png?​400 |}} 
- 
-  * **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 ==== 
- 
-  * **Model Size** - number of elements[3]. Allows for 
-    * Comparison of models (before and after, model A and model B on same system) 
-    * Measure of progress 
-    * Prediction of work effort 
-  * **Design Metrics** 
-    * Degree of coupling between classes (e.g. number of interactions within a class) 
-    * Degree of inheritance (e.g. depth of inheritance) 
-    * Degree of cohesion 
-    * Degree of polymorphism 
-    * Degree of information hiding 
-    * Degree of complexity 
-  * **Comprehensibility Metrics** 
-    * Number of elements on diagram 
-    * Number of crossing lines on diagram 
-    * Number of entry and exit points on diagram 
- 
-==== References ==== 
- 
-  * [1] P. Mohagheghi et al, [[https://​www.omg.org/​ocsmp/​ICSE2009_WoSQ_3415_mohagheghi_parastoo.pdf|"​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, [[http://​www.win.tue.nl/​~clange/​papers/​Lange_ModelSizeMatters.pdf|"​Model Size Matters"​]],​ Proc. Model Size Metrics Workshop, 2006.  
 ===== Problem ===== ===== Problem =====
 {{ :​bok:​eng:​mbse:​intro:​week_03_still_03_b.jpg?​600 |}} {{ :​bok:​eng:​mbse:​intro:​week_03_still_03_b.jpg?​600 |}}
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 ===== Methodology ===== ===== Methodology =====
  
 +For a review of MBSE Methodologies see this [[bok:​eng:​mbse:​method|page]].
 ==== Method ==== ==== Method ====
  
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 === MBSE activity === === MBSE activity ===
 +  * Ask questions
 +    * Why model?
 +    * What problem are we trying to solve with the model?
   * Evaluate what functions (and at what level of fidelity) MBSE activity should be applied to   * Evaluate what functions (and at what level of fidelity) MBSE activity should be applied to
   * Evaluate what components (and at what level of fidelity) MBSE activity should be applied to   * Evaluate what components (and at what level of fidelity) MBSE activity should be applied to
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     * Use Cases     * Use Cases
       * Nominal Scenarios - covers basic outcomes under nominal conditions and does not consider exceptions       * Nominal Scenarios - covers basic outcomes under nominal conditions and does not consider exceptions
-  * [[mbse:​critique|Critique]]+  * [[bok:eng:mbse:​critique|Critique]]
  
 ===== Use Cases ===== ===== Use Cases =====
bok/eng/mbse/intro.1593940728.txt.gz ยท Last modified: 2020/07/05 09:18 by anwlur