Adding Random Operations to OCL
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This slides correspond to the talk we gave at the MODEVVA'17 workshop. This work presents an extension of OCL to allow modellers to deal with random numbers and probability distributions in their OCL specifications. We show its implementation in the tool USE and discuss some advantages of this new feature for the validation and verification of models.
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Abstract: Uncertainty is the quality or state that involves lacking information or insufficient knowledge. Uncertainty can be due to different reasons, including incomplete or inaccurate information, inexact data or measurements, imprecise human judgments, or approximate estimations. The explicit representation of uncertainty is gaining attention among software engineers in order to provide more faithful systems representations, more accurate design methods, and better estimations of the development processes. However, incorporating uncertainty into our systems models is not enough. Uncertainty also affects many aspects related to the quality of systems, products, processes, and data, including how uncertainty is taken into account when designing our systems, measured when evaluating their quality, and perceived by customers and users. In fact, uncertainty – and, more specifically, the lack of knowledge about the system, our measuring tools, and our potential users – should be incorporated into our quality models, too. This talk identifies several kinds of uncertainties that have a direct impact on quality, and discusses some challenges on how quality needs to be planned, modeled, designed, measured and ensured in the presence of uncertainty.
Research Evaluation - The current situation in Spain
1. Research evaluation in Spain has improved with new agencies adopting the GGS conference rating system, equating computer science conferences to journals.
2. The Spanish Informatics Societies play a key role in harmonizing evaluation criteria, building on successes like GGS, but challenges remain like dependence on journal impact factors.
3. Opportunities now exist to promote positive changes with momentum from initiatives addressing issues, and experiences can be shared across Europe for further improvement.
Belief Uncertainty in Software Models
The document discusses representing belief uncertainty in software models. It proposes using a Bayesian probability approach to quantify belief uncertainty, where degrees of belief for model statements are assigned by belief agents. A UML profile and operational semantics are defined to explicitly represent belief agents and propagate credence values through dependent statements. This allows querying credence values to understand the level of confidence in different parts of the model based on the assessing agent. Future work includes associating evidence with beliefs and applying this approach to other model types.
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Towards a Body of Knowledge for Model-Based Software Engineering
Model-based Software Engineering (MBSE) is now accepted as a Software Engineering (SE) discipline and is being taught as part of more general SE curricula. However, an agreed core of concepts, mechanisms and practices — which constitutes the Body of Knowledge of a discipline — has not been captured anywhere, and is only partially covered by the SE Body of Knowledge (SWEBOK). With the goals of characterizing the contents of the MBSE discipline, promoting a consistent view of it worldwide, clarifying its scope with regard to other SE disciplines, and defining a foundation for a curriculum development on MBSE, this paper provides a proposal
for an extension of the contents of SWEBOK with the set of fundamental concepts, terms and mechanisms that should constitute the MBSE Body of Knowledge.
La Ingeniería Informática no es una Ciencia -- Reflexiones sobre la Educación...
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Esta charla está dedicada a presentar, y debatir, lo que representan estas novedades para los estudiantes de doctorado en Informática, y sugerir algunos aspectos que es importante tener en cuenta a la hora de plantear el desarrollo de la tesis y construir nuestra carrera profesional.
Accountable objects: Modeling Liability in Open Distributed Systems
As an increasing amount of commercial activity becomes automated, the importance of techniques for providing complete system specifications, checking the correctness of interactions and flagging incorrect behaviour increases. The aim throughout is to generate more complete information about the system and so to produce IT solutions that reflect the business requirements accurately. So far, most efforts have been placed on the appropriate specification of the system behaviour and then on the non-functional requirements that constitute the contract between a system and its users. But in fully-automated commercial systems, such as Cloud Computing or SOA systems, we should also consider the liability of the different parties, since we should be able that assign responsibility to objects and, more importantly, to know in case of problems or contact violations, which one should be blamed.
The consequence of these considerations is that we need the ability to express more directly the necessary obligations and other deontic concepts, such as permissions and prohibitions, giving the designer the tools for extending the behavioural information to make it clear where obligations apply and with what detailed properties. In this talk we describe current activities within the International Organization for Standardization (ISO) to extend the ODP family of standards for the expression of policies using deontic logic, and on how to improve support for deontic concepts based on their reification.
Models And Meanings
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Improving Naming and Grouping in UML
Slides of the talk at ECMDA 2011, Brimingham, June 2011
ABSTRACT:
The package is one of the basic UML concepts. It is used both to group model elements and to provide a namescope for its members. However, combining these two tasks into a single UML concept can become not only too restrictive but also a source of subtle problems. This paper presents some improvements to the current UML naming and grouping schemata, using the ideas proposed in the reference model of Open Distributed Processing (ODP). The extensions try to maintain backwards compatibility with the existing UML concepts, while allowing more flexible grouping and naming mechanisms.
On the Combination of Domain Specific Modeling Languages
This are the slides of the presentation at ECMFA 2010 of paper:
"On the Combination of Domain Specific Modeling Languages". LNCS 6138, pp. 301-316, Paris, June 16-18, 2010.
ABSTRACT: Domain Specific Modeling Languages (DSMLs) are essential elements in Model-based Engineering. Each DSML allows capturing certain properties of the system, while abstracting other properties away. Nowadays DSMLs are mostly used in silos to solve specific problems. However, there are many occasions when multiple DSMLs need to be combined to design systems in a modular way. In this paper we discuss some scenarios of use and several mechanisms for DSML combination. We propose a general framework for combining DSMLs that subsumes them, based on the concept of viewpoint unification, and its realization using model-driven techniques.
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- 1. Adding Random Operations to OCL Antonio Vallecillo, Martin Gogolla SEPTEMBER 19, 2017
- 2. Random in OCL • Uncertainty is an essential element of many of the systems we have to model • Values of properties representing measures • Decisions and branches • Number of elements of populations • Expected durations, costs, tolerance,… • Random numbers and probability distributions can be of help here • They permit combining definite knowledge at one level with uncertain values • Expectations and assumptions that remain uncertain or imprecise at high- level, can be made precise and realized by stating the corresponding percentages or the probability distributions that parameters or values follow 2
- 3. Random in OCL • Current OCL specification does not provide support for random numbers or probability distributions • Indeterminism only provided by “any()” operation on OCL Collections 11.9.1 Collection any() Returns any element in the source collection for which body evaluates to true. Returns invalid if any body evaluates to invalid for any element, otherwise if there are one or more elements for which body is true, an indeterminate choice of one of them is returned, otherwise the result is invalid. source->any(iterator | body) = source->select(iterator | body)->asSequence()->first() 3
- 4. Indeterminism <> random • This is the common behaviour of OCL “any()” 4
- 5. OCL random operations context Real::rand() : Real post undetermined: if self > 0.0 then ( 0.0 <= result ) and ( result < self ) else if self < 0.0 then ( result <= 0.0 ) and ( self < result ) else result = self endif endif post statisticallyRandom: -- A sequence contains no recognizable patterns or regularities -- This property deserves its own discussion :-) context Integer::srnd() : Integer post seedGeneration: -- if self > 0 then self is the new seed -- else a new seed is automatically generated (time, etc.) -- in both cases it returns the previous seed 5
- 12. Implementation (1/2) <operation source="Real" name="rand" returnType="Real"> <body> <![CDATA[ $self * Random.rand ]]> </body> </operation> <operation source="Integer" name="srand“ returnType="Integer"> <body> <![CDATA[ if $self > 0 return Random.srand($self) % 1000000 else return Random.srand() % 10000000 end ]]> </body> </operation> 12
- 13. Implementation (2/2) • Employing USE extension mechanism • Ruby implementation of newly defined operations 13
- 14. 14
- 15. Conclusions • Random numbers and Probability distributions permit dealing with uncertainty at different levels • Values of parameters and attributes • Number of objects and links • Size of test samples and values • Beyond the basic indeterminism currently provided by OCL. • Implementation available at https://www.dropbox.com/s/2j9tgejbj507id0/oclextensions.zip?dl=0 15
- 16. Thanks!
- 17. Adding Random Operations to OCL Antonio Vallecillo, Martin Gogolla SEPTEMBER 19, 2017