SlideShare a Scribd company logo
1 of 23
Download to read offline
Expressing Measurement Uncertainty
in OCL/UML Models
Toulouse, June 27, 2018
M. F. Bertoa1, N. Moreno1, G. Barquero1, L. Burgueño1, J.Troya2 and A. Vallecillo1
1 Atenea Research Group, Univ. Málaga, Spain
2 ISA Group, Univ. Sevilla, Spain
ECMFA 2018
Motivation
Uncertainty in Engineering Disciplines
 Engineers naturally think about:
 uncertainty associated with measured values
 Uncertainty is explicitly defined in their models
and considered in model-based simulations
2
Motivation
However the situation is not the same when modeled in software! 
3
Measurement Uncertainty in Software Models
Some Attempts 
context RectangleSw::area() : Real = h*w
4
Uncertainty in Software Engineering
 Very limited support for representing uncertainty in software models
 No support for considering such properties in model-based simulations
 Not part of their type systems!
Motivation
Measure
value : Real
What is the uncertainty of the
measurement method?
How is this uncertainty propagated when
making calculations with uncertain
values?
5
Abstraction vs Precision
“Being abstract is something profoundly different from being
vague... The purpose of abstraction is not to be vague, but to
create a new semantic level in which one can be absolutely
precise.”
Edsger Dijkstra
6
(Magnitudes and) Uncertainty Representation of Uncertainty
Definition: Standard Uncertainty [GUM]
 Uncertainty of the result of a measurement 𝑥𝑥 expressed as a standard
deviation 𝑢𝑢 of the possible variation of the values of 𝑥𝑥.
 Representation: 𝑥𝑥 ± 𝑢𝑢 or 𝑥𝑥, 𝑢𝑢
 Examples:
[GUM] JCGM 100:2008. Evaluation of measurement data – Guide to the expression of uncertainty in measurement.
http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf
Normal distribution: (𝑥𝑥, 𝜎𝜎) with mean 𝑥𝑥, standard deviation 𝜎𝜎
Interval 𝑎𝑎, 𝑏𝑏 : Uniform or rectangular distribution is assumed
(𝑥𝑥, 𝑢𝑢) with 𝑥𝑥 =
𝑎𝑎+𝑏𝑏
2
, 𝑢𝑢 =
(𝑏𝑏−𝑎𝑎)
2 3
7
Measurement Uncertainty Operations
 Computations with uncertain values have to respect the propagation of
uncertainty (uncertainty analysis)
Two Methods for Computing Aggregated Uncertainty
 Normal or Uniform distribution: Analytical (closed-form) solutions
 General case: Using samples (SIPMath Std.)
A. Vallecillo, C. Morcillo, and P. Orue. Expressing Measurement Uncertainty in Software Models. In Proc. of
QUATIC 2016, pages 1–10, 2016.
8
Our proposal
 Extension of the OCL/UML
 Primitive types
 Collections
Ongoing
work
9
Our proposal
 Extension of the OCL/UML
 Primitive types
 extension based on subtyping
10
UReal
 UReal are pairs (x, u)
 x : Real represents the (expected, estimated or actual) value
 u : Real represents the uncertainty, expressed as a standard deviation of the
possible variation of the values of 𝑥𝑥.
 Real numbers correspond to pairs (x, 0.0)
 Example:
(2.0, 0.3)
(2.5, 0.25)
11
UReal
 Constants
 UReal(3.0, 0.05), UReal (0.0, 0.0), UReal (-1.0, 0.003)
 Operations
 Arithmetic
 Trigonometric
 Comparisons
 Conversions
 …
 Examples (in OCL):
12
UReal - Operations
 Operations. Comparisons
 Should return probabilities, rather than crisp Boolean values
a=(2.0, 0.3)
b=(2.5, 0.25)
d=(1, 0.5)
c=(1, 0.75)
a<b  (true, 0.893)
a=b  (true, 0.106)
a>b  (true, 1.11∙10-16 )
c<d  (true, 0.152)
c=d  (true, 0.754)
c>d  (true, 0.094) 13
UInteger and UUnlimitedNatural
 UInteger
 Pairs (n, u)
 n : Integer represents the (expected, estimated or actual) value
 u : Real represents the uncertainty, expressed as a standard deviation of the possible
variation of the values of n.
 Integer numbers correspond to pairs (n, 0.0)
 UInteger operations
 Behavior defined by lifting the operation to UInteger and then projecting the result if
needed
 UUnlimitedNatural
 non-negative Integer or a special unlimited value (*)
 (n, u) where n:Integer, u:Real, n≥0
 The uncertainty of * is always 0.0
 Operations not involving special value * are defined by lifting them to UInteger
 Comparison operations need to consider the particular case of special value *,
lifting the operation to the supertype if this value is not involved
14
UBoolean
 UBooleans are pairs (b, c)
 where b:Boolean and c:Real, c ϵ [0, 1]
 c represents the confidence that the actual value of the value is indeed b
 Canonical form: (true, c)
 Equivalence relation: (b, c) = (not b, 1 - c)
 Constants
 UBoolean(true, 0.999), UBoolean(false, 0.001)
 Operations
 Redefined basic operations: and, or, not
 Redefined secondary operations: implies, equivalent, xor
 Kept equals (=) and distinct (<>) w/o uncertainty and,
 Added operations uEquals():UBoolean and uDistinct():UBoolean
 Conversion operations: toBoolean() and toBooleanC(c:Real)
15
UBoolean
 Operations
 Two implementations:
 Assuming all values are independents: Analytical specification:
 When no assumption can be made about the independence: Monte-Carlo simulation
method:
16
Collections
 Extension based on the extended operators for the primitive datatypes
 uForAll(), uExists(), uIncludes(), uExcludes(), uSelect(), …
 Examples:
17
Trains
* Note that in the implementation in USE the uncertain types are included as basic primitive data types, as well as their native operations
Trains
 Train arrival time: T = 44.560 ± 10.581
 User arrival time: M= 40.045 ± 5.704
 Their diference: T-M = 4.515 ± 12.374
 M + 3 <= T  (true; 0.887)
 The probability that the user catches the train is 0.887
19
MARTE and SysML
 Compatibility problems when combining MARTE and SysML models
NOTE: Of course, simulation tools (Modelica, Matlab/Simulink) and mathematic languages (Mathematica) provide
support for units, dimensions and uncertainty, but they are at a different abstraction level
 MARTE has stereotypes to decorate values with information
about the units and with measurement uncertainty
(“precision”)
 However:
 It is simply “decorative information”: no type checking, no
operations for aggregating uncertainty values
 SysML 1.4 provides the QUDV (Quantities, Units,
Dimensions) and ISO 80000 library with all units and
dimensions.
 However:
 No support for dealing with measurement uncertainty
20
Conclusions and future work
 Extension of OCL/UML datatypes to capture and manipulate properties of physical
systems, in particular measurement uncertainty
 Implementations available for Java and USE
21
Conclusions and future work
 Extension of OCL/UML datatypes to capture and manipulate properties of
physical systems, in particular measurement uncertainty
 Implementations available for Java and USE
 Ongoing and Future Work
 Cover the rest of the UML/OCL datatypes: String and Enum
 Mathematical properties
 Mappings from our specifications to simulation languages (Modelica, Simulink)
 Further validation of our proposal:
 case studies
 expressiveness
 applicability
22
Expressing Measurement Uncertainty
in OCL/UML Models
Toulouse, June 27, 2018
M. F. Bertoa1, N. Moreno1, G. Barquero1, L. Burgueño1, J.Troya2 and A. Vallecillo1
1 Atenea Research Group, Univ. Málaga, Spain,
2 ISA Group, Univ. Sevilla, Spain
ECMFA 2018
Thanks!

More Related Content

Recently uploaded

nvidia AI-gtc 2024 partial slide deck.pptx
nvidia AI-gtc 2024 partial slide deck.pptxnvidia AI-gtc 2024 partial slide deck.pptx
nvidia AI-gtc 2024 partial slide deck.pptxjasonsedano2
 
Mohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxMohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxKISHAN KUMAR
 
Graphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesGraphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesDIPIKA83
 
cloud computing notes for anna university syllabus
cloud computing notes for anna university syllabuscloud computing notes for anna university syllabus
cloud computing notes for anna university syllabusViolet Violet
 
Landsman converter for power factor improvement
Landsman converter for power factor improvementLandsman converter for power factor improvement
Landsman converter for power factor improvementVijayMuni2
 
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxVertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxLMW Machine Tool Division
 
Nodal seismic construction requirements.pptx
Nodal seismic construction requirements.pptxNodal seismic construction requirements.pptx
Nodal seismic construction requirements.pptxwendy cai
 
A Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationA Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationMohsinKhanA
 
Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Bahzad5
 
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfSummer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfNaveenVerma126
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Sean Meyn
 
Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...sahb78428
 
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdfsdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdfJulia Kaye
 
Phase noise transfer functions.pptx
Phase noise transfer      functions.pptxPhase noise transfer      functions.pptx
Phase noise transfer functions.pptxSaiGouthamSunkara
 
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxSUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxNaveenVerma126
 
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Apollo Techno Industries Pvt Ltd
 
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Amil baba
 
Renewable Energy & Entrepreneurship Workshop_21Feb2024.pdf
Renewable Energy & Entrepreneurship Workshop_21Feb2024.pdfRenewable Energy & Entrepreneurship Workshop_21Feb2024.pdf
Renewable Energy & Entrepreneurship Workshop_21Feb2024.pdfodunowoeminence2019
 
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratoryدليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide LaboratoryBahzad5
 

Recently uploaded (20)

nvidia AI-gtc 2024 partial slide deck.pptx
nvidia AI-gtc 2024 partial slide deck.pptxnvidia AI-gtc 2024 partial slide deck.pptx
nvidia AI-gtc 2024 partial slide deck.pptx
 
Mohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptxMohs Scale of Hardness, Hardness Scale.pptx
Mohs Scale of Hardness, Hardness Scale.pptx
 
Graphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesGraphics Primitives and CG Display Devices
Graphics Primitives and CG Display Devices
 
cloud computing notes for anna university syllabus
cloud computing notes for anna university syllabuscloud computing notes for anna university syllabus
cloud computing notes for anna university syllabus
 
Landsman converter for power factor improvement
Landsman converter for power factor improvementLandsman converter for power factor improvement
Landsman converter for power factor improvement
 
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxVertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
 
Nodal seismic construction requirements.pptx
Nodal seismic construction requirements.pptxNodal seismic construction requirements.pptx
Nodal seismic construction requirements.pptx
 
A Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software SimulationA Seminar on Electric Vehicle Software Simulation
A Seminar on Electric Vehicle Software Simulation
 
Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)
 
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfSummer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
 
Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...Clutches and brkesSelect any 3 position random motion out of real world and d...
Clutches and brkesSelect any 3 position random motion out of real world and d...
 
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdfsdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
sdfsadopkjpiosufoiasdoifjasldkjfl a asldkjflaskdjflkjsdsdf
 
Phase noise transfer functions.pptx
Phase noise transfer      functions.pptxPhase noise transfer      functions.pptx
Phase noise transfer functions.pptx
 
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docxSUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
SUMMER TRAINING REPORT ON BUILDING CONSTRUCTION.docx
 
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...Technology Features of Apollo HDD Machine, Its Technical Specification with C...
Technology Features of Apollo HDD Machine, Its Technical Specification with C...
 
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
 
Renewable Energy & Entrepreneurship Workshop_21Feb2024.pdf
Renewable Energy & Entrepreneurship Workshop_21Feb2024.pdfRenewable Energy & Entrepreneurship Workshop_21Feb2024.pdf
Renewable Energy & Entrepreneurship Workshop_21Feb2024.pdf
 
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratoryدليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
دليل تجارب الاسفلت المختبرية - Asphalt Experiments Guide Laboratory
 
Présentation IIRB 2024 Chloe Dufrane.pdf
Présentation IIRB 2024 Chloe Dufrane.pdfPrésentation IIRB 2024 Chloe Dufrane.pdf
Présentation IIRB 2024 Chloe Dufrane.pdf
 

Expressing Measurement Uncertainty in OCL/UML Models

  • 1. Expressing Measurement Uncertainty in OCL/UML Models Toulouse, June 27, 2018 M. F. Bertoa1, N. Moreno1, G. Barquero1, L. Burgueño1, J.Troya2 and A. Vallecillo1 1 Atenea Research Group, Univ. Málaga, Spain 2 ISA Group, Univ. Sevilla, Spain ECMFA 2018
  • 2. Motivation Uncertainty in Engineering Disciplines  Engineers naturally think about:  uncertainty associated with measured values  Uncertainty is explicitly defined in their models and considered in model-based simulations 2
  • 3. Motivation However the situation is not the same when modeled in software!  3
  • 4. Measurement Uncertainty in Software Models Some Attempts  context RectangleSw::area() : Real = h*w 4
  • 5. Uncertainty in Software Engineering  Very limited support for representing uncertainty in software models  No support for considering such properties in model-based simulations  Not part of their type systems! Motivation Measure value : Real What is the uncertainty of the measurement method? How is this uncertainty propagated when making calculations with uncertain values? 5
  • 6. Abstraction vs Precision “Being abstract is something profoundly different from being vague... The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise.” Edsger Dijkstra 6
  • 7. (Magnitudes and) Uncertainty Representation of Uncertainty Definition: Standard Uncertainty [GUM]  Uncertainty of the result of a measurement 𝑥𝑥 expressed as a standard deviation 𝑢𝑢 of the possible variation of the values of 𝑥𝑥.  Representation: 𝑥𝑥 ± 𝑢𝑢 or 𝑥𝑥, 𝑢𝑢  Examples: [GUM] JCGM 100:2008. Evaluation of measurement data – Guide to the expression of uncertainty in measurement. http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf Normal distribution: (𝑥𝑥, 𝜎𝜎) with mean 𝑥𝑥, standard deviation 𝜎𝜎 Interval 𝑎𝑎, 𝑏𝑏 : Uniform or rectangular distribution is assumed (𝑥𝑥, 𝑢𝑢) with 𝑥𝑥 = 𝑎𝑎+𝑏𝑏 2 , 𝑢𝑢 = (𝑏𝑏−𝑎𝑎) 2 3 7
  • 8. Measurement Uncertainty Operations  Computations with uncertain values have to respect the propagation of uncertainty (uncertainty analysis) Two Methods for Computing Aggregated Uncertainty  Normal or Uniform distribution: Analytical (closed-form) solutions  General case: Using samples (SIPMath Std.) A. Vallecillo, C. Morcillo, and P. Orue. Expressing Measurement Uncertainty in Software Models. In Proc. of QUATIC 2016, pages 1–10, 2016. 8
  • 9. Our proposal  Extension of the OCL/UML  Primitive types  Collections Ongoing work 9
  • 10. Our proposal  Extension of the OCL/UML  Primitive types  extension based on subtyping 10
  • 11. UReal  UReal are pairs (x, u)  x : Real represents the (expected, estimated or actual) value  u : Real represents the uncertainty, expressed as a standard deviation of the possible variation of the values of 𝑥𝑥.  Real numbers correspond to pairs (x, 0.0)  Example: (2.0, 0.3) (2.5, 0.25) 11
  • 12. UReal  Constants  UReal(3.0, 0.05), UReal (0.0, 0.0), UReal (-1.0, 0.003)  Operations  Arithmetic  Trigonometric  Comparisons  Conversions  …  Examples (in OCL): 12
  • 13. UReal - Operations  Operations. Comparisons  Should return probabilities, rather than crisp Boolean values a=(2.0, 0.3) b=(2.5, 0.25) d=(1, 0.5) c=(1, 0.75) a<b  (true, 0.893) a=b  (true, 0.106) a>b  (true, 1.11∙10-16 ) c<d  (true, 0.152) c=d  (true, 0.754) c>d  (true, 0.094) 13
  • 14. UInteger and UUnlimitedNatural  UInteger  Pairs (n, u)  n : Integer represents the (expected, estimated or actual) value  u : Real represents the uncertainty, expressed as a standard deviation of the possible variation of the values of n.  Integer numbers correspond to pairs (n, 0.0)  UInteger operations  Behavior defined by lifting the operation to UInteger and then projecting the result if needed  UUnlimitedNatural  non-negative Integer or a special unlimited value (*)  (n, u) where n:Integer, u:Real, n≥0  The uncertainty of * is always 0.0  Operations not involving special value * are defined by lifting them to UInteger  Comparison operations need to consider the particular case of special value *, lifting the operation to the supertype if this value is not involved 14
  • 15. UBoolean  UBooleans are pairs (b, c)  where b:Boolean and c:Real, c ϵ [0, 1]  c represents the confidence that the actual value of the value is indeed b  Canonical form: (true, c)  Equivalence relation: (b, c) = (not b, 1 - c)  Constants  UBoolean(true, 0.999), UBoolean(false, 0.001)  Operations  Redefined basic operations: and, or, not  Redefined secondary operations: implies, equivalent, xor  Kept equals (=) and distinct (<>) w/o uncertainty and,  Added operations uEquals():UBoolean and uDistinct():UBoolean  Conversion operations: toBoolean() and toBooleanC(c:Real) 15
  • 16. UBoolean  Operations  Two implementations:  Assuming all values are independents: Analytical specification:  When no assumption can be made about the independence: Monte-Carlo simulation method: 16
  • 17. Collections  Extension based on the extended operators for the primitive datatypes  uForAll(), uExists(), uIncludes(), uExcludes(), uSelect(), …  Examples: 17
  • 18. Trains * Note that in the implementation in USE the uncertain types are included as basic primitive data types, as well as their native operations
  • 19. Trains  Train arrival time: T = 44.560 ± 10.581  User arrival time: M= 40.045 ± 5.704  Their diference: T-M = 4.515 ± 12.374  M + 3 <= T  (true; 0.887)  The probability that the user catches the train is 0.887 19
  • 20. MARTE and SysML  Compatibility problems when combining MARTE and SysML models NOTE: Of course, simulation tools (Modelica, Matlab/Simulink) and mathematic languages (Mathematica) provide support for units, dimensions and uncertainty, but they are at a different abstraction level  MARTE has stereotypes to decorate values with information about the units and with measurement uncertainty (“precision”)  However:  It is simply “decorative information”: no type checking, no operations for aggregating uncertainty values  SysML 1.4 provides the QUDV (Quantities, Units, Dimensions) and ISO 80000 library with all units and dimensions.  However:  No support for dealing with measurement uncertainty 20
  • 21. Conclusions and future work  Extension of OCL/UML datatypes to capture and manipulate properties of physical systems, in particular measurement uncertainty  Implementations available for Java and USE 21
  • 22. Conclusions and future work  Extension of OCL/UML datatypes to capture and manipulate properties of physical systems, in particular measurement uncertainty  Implementations available for Java and USE  Ongoing and Future Work  Cover the rest of the UML/OCL datatypes: String and Enum  Mathematical properties  Mappings from our specifications to simulation languages (Modelica, Simulink)  Further validation of our proposal:  case studies  expressiveness  applicability 22
  • 23. Expressing Measurement Uncertainty in OCL/UML Models Toulouse, June 27, 2018 M. F. Bertoa1, N. Moreno1, G. Barquero1, L. Burgueño1, J.Troya2 and A. Vallecillo1 1 Atenea Research Group, Univ. Málaga, Spain, 2 ISA Group, Univ. Sevilla, Spain ECMFA 2018 Thanks!