Action semantics of unified modeling language

The Uni ed Modeling Language or UML, as a visual and general purpose modeling language, has been around for more than a decade, gaining increasingly wide application and becoming the de-facto industrial standard for modeling software systems. However, the dynamic semantics of UML behaviours are only described in natural languages. Speci cation in natural languages inevitably involves vagueness, lacks reasonability and discourages mechanical language implementation. Such semi-formality of UML causes wide concern for researchers, including us. The formal semantics of UML demands more readability and extensibility due to its fast evolution and a wider range of users. Therefore we adopt Action Semantics (AS), mainly created by Peter Mosses, to formalize the dynamic semantics of UML, because AS can satisfy these needs advantageously compared to other frameworks. Instead of de ning UML directly, we design an action language, called ALx, and use it as the intermediary between a typical executable UML and its action semantics. ALx is highly heterogeneous, combining the features of Object Oriented Programming Languages, Object Query Languages, Model Description Languages and more complex behaviours like state machines. Adopting AS to formalize such a heterogeneous language is in turn of signi cance in exploring the adequacy and applicability of AS. In order to give assurance of the validity of the action semantics of ALx, a prototype ALx-to-Java translator is implemented, underpinned by our formal semantic description of the action language and using the Model Driven Approach (MDA). We argue that MDA is a feasible way of implementing this source-to-source language translator because the cornerstone of MDA, UML, is adequate to specify the static aspect of programming languages, and MDA provides executable transformation languages to model mapping rules between languages. We also construct a translator using a commonly-used conventional approach, in i which a tool is employed to generate the lexical scanner and the parser, and then other components including the type checker, symbol table constructor, intermediate representation producer and code generator, are coded manually. Then we compare the conventional approach with the MDA. The result shows that MDA has advantages over the conventional method in the aspect of code quality but is inferior to the latter in terms of system performance

Functional metamodels for systems and software

International audienceThe modeling, analysis and design of systems is generally based on many formalisms to describe discrete and/or continuous behaviors, and to map these descriptions into a specific platform. In this context, the article proposes the concept of functional metamodeling to capture, then to integrate modeling languages. The concept offers an alternative to standard Model Driven Engineering (MDE) and is well adapted to mathematical descriptions such as the ones found in system modeling. As an application, a set of functional metamodels is proposed for dataflows (usable to model continuous behaviors), statetransition systems (usable to model discrete behaviors) and a metamodel for actions (to model interactions with a target platform and concurrent execution). A model of a control architecture for a legged robot is proposed as an application of these modeling languages

A formal framework for model management

El Desarrollo de Software Dirigido por Modelos es una rama de la Ingeniería del Software en la que los artefactos software se representan como modelos para incrementar la productividad, calidady eficiencia económica en el proceso de desarrollo de software, donde un modelo proporciona una representación abstracta del código final de una aplicación. En este campo, la iniciativa Model-Driven Architecture (MDA), patrocinada por la OMG, está constituida por una familia de estándares industriales, entre los que se destacan: Meta-Object Facility (MOF), Unified Modeling Language (UML), Object Constraint Language (OCL), XML Metadata Interchange (XMI), y Query/Views/Transformations (QVT). Estos estándares proporcionan unas directrices comunes para herramientas basadas en modelos y para procesos de desarrollo de software dirigidos por modelos. Su objetivo consiste en mejorar la interoperabilidad entre marcos de trabajo ejecutables, en automatizar el proceso desarrollo de software de software y en proporcionar técnicas que eviten errores durante ese proceso. El estándar MOF describe un marco de trabajo genérico que permite definir la sintaxis abstracta de lenguajes de modelado. Este estándar persigue la definición de los conceptos básicos que son utilizados en procesos de desarrollo de software dirigidos por modelos: que es un modelo, que es un metamodelo, qué es reflexión en un marco de trabajo basado en MOF, etc. Sin embargo, la mayoría de estos conceptos carecen de una semántica formal en la versión actual del estándar MOF. Además, OCL se utiliza como un lenguage de definición de restricciones que permite añadir semántica a un metamodelo MOF. Desafortunadamente, la relación entre un metamodelo y sus restricciones OCL también carece de una semántica formal. Este hecho es debido, en parte, a que los metamodelos solo pueden ser definidos como dato en un marco de trabajo basado en MOF. El estándar MOF también proporciona las llamadas facilidades de reflexión de MOF (MOF ReflectiBoronat Moll, A. (2007). A formal framework for model management [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1964Palanci

Multi-paradigm modelling for cyber–physical systems: a descriptive framework

The complexity of cyber–physical systems (CPSS) is commonly addressed through complex workflows, involving models in a plethora of different formalisms, each with their own methods, techniques, and tools. Some workflow patterns, combined with particular types of formalisms and operations on models in these formalisms, are used successfully in engineering practice. To identify and reuse them, we refer to these combinations of workflow and formalism patterns as modelling paradigms. This paper proposes a unifying (Descriptive) Framework to describe these paradigms, as well as their combinations. This work is set in the context of Multi-Paradigm Modelling (MPM), which is based on the principle to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s) and workflows. The purpose of the Descriptive Framework presented in this paper is to serve as a basis to reason about these formalisms, workflows, and their combinations. One crucial part of the framework is the ability to capture the structural essence of a paradigm through the concept of a paradigmatic structure. This is illustrated informally by means of two example paradigms commonly used in CPS: Discrete Event Dynamic Systems and Synchronous Data Flow. The presented framework also identifies the need to establish whether a paradigm candidate follows, or qualifies as, a (given) paradigm. To illustrate the ability of the framework to support combining paradigms, the paper shows examples of both workflow and formalism combinations. The presented framework is intended as a basis for characterisation and classification of paradigms, as a starting point for a rigorous formalisation of the framework (allowing formal analyses), and as a foundation for MPM tool development

Formal transformation methods for automated fault tree generation from UML diagrams

With a growing complexity in safety critical systems, engaging Systems Engineering with System Safety Engineering as early as possible in the system life cycle becomes ever more important to ensure system safety during system development. Assessing the safety and reliability of system architectural design at the early stage of the system life cycle can bring value to system design by identifying safety issues earlier and maintaining safety traceability throughout the design phase. However, this is not a trivial task and can require upfront investment. Automated transformation from system architecture models to system safety and reliability models offers a potential solution. However, existing methods lack of formal basis. This can potentially lead to unreliable results. Without a formal basis, Fault Tree Analysis of a system, for example, even if performed concurrently with system design may not ensure all safety critical aspects of the design. [Continues.]</div

Functional Ontologies and Their Application to Hydrologic Modeling: Development of an Integrated Semantic and Procedural Knowledge Model and Reasoning Engine

This dissertation represents the research and development of new concepts and techniques for modeling the knowledge about the many concepts we as hydrologists must understand such that we can execute models that operate in terms of conceptual abstractions and have those abstractions translate to the data, tools, and models we use every day. This hydrologic knowledge includes conceptual (i.e. semantic) knowledge, such as the hydrologic cycle concepts and relationships, as well as functional (i.e. procedural) knowledge, such as how to compute the area of a watershed polygon, average basin slope or topographic wetness index. This dissertation is presented as three papers and a reference manual for the software created. Because hydrologic knowledge includes both semantic aspects as well as procedural aspects, we have developed, in the first paper, a new form of reasoning engine and knowledge base that extends the general-purpose analysis and problem-solving capability of reasoning engines by incorporating procedural knowledge, represented as computer source code, into the knowledge base. The reasoning engine is able to compile the code and then, if need be, execute the procedural code as part of a query. The potential advantage to this approach is that it simplifies the description of procedural knowledge in a form that can be readily utilized by the reasoning engine to answer a query. Further, since the form of representation of the procedural knowledge is source code, the procedural knowledge has the full capabilities of the underlying language. We use the term functional ontology to refer to the new semantic and procedural knowledge models. The first paper applies the new knowledge model to describing and analyzing polygons. The second and third papers address the application of the new functional ontology reasoning engine and knowledge model to hydrologic applications. The second paper models concepts and procedures, including running external software, related to watershed delineation. The third paper models a project scenario that includes integrating several models. A key advance demonstrated in this paper is the use of functional ontologies to apply metamodeling concepts in a manner that both abstracts and fully utilizes computational models and data sets as part of the project modeling process

Assessing and improving the quality of model transformations

Software is pervading our society more and more and is becoming increasingly complex. At the same time, software quality demands remain at the same, high level. Model-driven engineering (MDE) is a software engineering paradigm that aims at dealing with this increasing software complexity and improving productivity and quality. Models play a pivotal role in MDE. The purpose of using models is to raise the level of abstraction at which software is developed to a level where concepts of the domain in which the software has to be applied, i.e., the target domain, can be expressed e??ectively. For that purpose, domain-speci??c languages (DSLs) are employed. A DSL is a language with a narrow focus, i.e., it is aimed at providing abstractions speci??c to the target domain. This makes that the application of models developed using DSLs is typically restricted to describing concepts existing in that target domain. Reuse of models such that they can be applied for di??erent purposes, e.g., analysis and code generation, is one of the challenges that should be solved by applying MDE. Therefore, model transformations are typically applied to transform domain-speci??c models to other (equivalent) models suitable for di??erent purposes. A model transformation is a mapping from a set of source models to a set of target models de??ned as a set of transformation rules. MDE is gradually being adopted by industry. Since MDE is becoming more and more important, model transformations are becoming more prominent as well. Model transformations are in many ways similar to traditional software artifacts. Therefore, they need to adhere to similar quality standards as well. The central research question discoursed in this thesis is therefore as follows. How can the quality of model transformations be assessed and improved, in particular with respect to development and maintenance? Recall that model transformations facilitate reuse of models in a software development process. We have developed a model transformation that enables reuse of analysis models for code generation. The semantic domains of the source and target language of this model transformation are so far apart that straightforward transformation is impossible, i.e., a semantic gap has to be bridged. To deal with model transformations that have to bridge a semantic gap, the semantics of the source and target language as well as possible additional requirements should be well understood. When bridging a semantic gap is not straightforward, we recommend to address a simpli??ed version of the source metamodel ??rst. Finally, the requirements on the transformation may, if possible, be relaxed to enable automated model transformation. Model transformations that need to transform between models in di??erent semantic domains are expected to be more complex than those that merely transform syntax. The complexity of a model transformation has consequences for its quality. Quality, in general, is a subjective concept. Therefore, quality can be de??ned in di??erent ways. We de??ned it in the context of model transformation. A model transformation can either be considered as a transformation de??nition or as the process of transforming a source model to a target model. Accordingly, model transformation quality can be de??ned in two di??erent ways. The quality of the de??nition is referred to as its internal quality. The quality of the process of transforming a source model to a target model is referred to as its external quality. There are also two ways to assess the quality of a model transformation (both internal and external). It can be assessed directly, i.e., by performing measurements on the transformation de??nition, or indirectly, i.e., by performing measurements in the environment of the model transformation. We mainly focused on direct assessment of internal quality. However, we also addressed external quality and indirect assessment. Given this de??nition of quality in the context of model transformations, techniques can be developed to assess it. Software metrics have been proposed for measuring various kinds of software artifacts. However, hardly any research has been performed on applying metrics for assessing the quality of model transformations. For four model transformation formalisms with di??fferent characteristics, viz., for ASF+SDF, ATL, Xtend, and QVTO, we de??ned sets of metrics for measuring model transformations developed with these formalisms. While these metric sets can be used to indicate bad smells in the code of model transformations, they cannot be used for assessing quality yet. A relation has to be established between the metric sets and attributes of model transformation quality. For two of the aforementioned metric sets, viz., the ones for ASF+SDF and for ATL, we conducted an empirical study aiming at establishing such a relation. From these empirical studies we learned what metrics serve as predictors for di??erent quality attributes of model transformations. Metrics can be used to quickly acquire insights into the characteristics of a model transformation. These insights enable increasing the overall quality of model transformations and thereby also their maintainability. To support maintenance, and also development in a traditional software engineering process, visualization techniques are often employed. For model transformations this appears as a feasible approach as well. Currently, however, there are few visualization techniques available tailored towards analyzing model transformations. One of the most time-consuming processes during software maintenance is acquiring understanding of the software. We expect that this holds for model transformations as well. Therefore, we presented two complementary visualization techniques for facilitating model transformation comprehension. The ??rst-technique is aimed at visualizing the dependencies between the components of a model transformation. The second technique is aimed at analyzing the coverage of the source and target metamodels by a model transformation. The development of the metric sets, and in particular the empirical studies, have led to insights considering the development of model transformations. Also, the proposed visualization techniques are aimed at facilitating the development of model transformations. We applied the insights acquired from the development of the metric sets as well as the visualization techniques in the development of a chain of model transformations that bridges a number of semantic gaps. We chose to solve this transformational problem not with one model transformation, but with a number of smaller model transformations. This should lead to smaller transformations, which are more understandable. The language on which the model transformations are de??ned, was subject to evolution. In particular the coverage visualization proved to be bene??cial for the co-evolution of the model transformations. Summarizing, we de??ned quality in the context of model transformations and addressed the necessity for a methodology to assess it. Therefore, we de??ned metric sets and performed empirical studies to validate whether they serve as predictors for model transformation quality. We also proposed a number of visualizations to increase model transformation comprehension. The acquired insights from developing the metric sets and the empirical studies, as well as the visualization tools, proved to be bene??cial for developing model transformations

A model driven approach to analysis and synthesis of sequence diagrams

Software design is a vital phase in a software development life cycle as it creates a blueprint for the implementation of the software. It is crucial that software designs are error-free since any unresolved design-errors could lead to costly implementation errors. To minimize these errors, the software community adopted the concept of modelling from various other engineering disciplines. Modelling provides a platform to create and share abstract or conceptual representations of the software system – leading to various modelling languages, among them Unified Modelling Language (UML) and Petri Nets. While Petri Nets strong mathematical capability allows various formal analyses to be performed on the models, UMLs user-friendly nature presented a more appealing platform for system designers. Using Multi Paradigm Modelling, this thesis presents an approach where system designers may have the best of both worlds; SD2PN, a model transformation that maps UML Sequence Diagrams into Petri Nets allows system designers to perform modelling in UML while still using Petri Nets to perform the analysis. Multi Paradigm Modelling also provided a platform for a well-established theory in Petri Nets – synthesis to be adopted into Sequence Diagram as a method of putting-together different Sequence Diagrams based on a set of techniques and algorithms

Quality assurance with dynamic meta modeling

Dynamic Meta Modeling (DMM) ist eine Semantikbeschreibungstechnik, die sich auf MOF-basierte Sprachen fokussiert und deren Verhalten durch graphische, operationale Regeln beschreibt. Der DMM-Ansatz wurde im Jahr 2000 von Engels et al. erstmals beschrieben und von Hausmann in 2006 in seiner Dissertation ausgearbeitet. Der nächste Schritt war nun, an verschiedenen Modellierungssprachen zu erproben, um die gemachten Erfahrungen in die Verbesserung von DMM und seinen Werkzeugen einfließen zu lassen. Das Ergebnis ist die DMM++-Methode, die in dieser Arbeit vorgestellt wird. Wir haben vorwiegend an drei Stellen Verbesserungen vorgenommen: Erstens haben wir basierend auf unseren Erfahrungen mit DMM neue Sprachkonzepte wie die Verfeinerung von Regeln entwickelt, und wir haben bestehende Konzepte wie die Behandlung von universell quantifizierten Strukturen oder Attributen verbessert. Zweitens haben wir einen testgetriebenen Semantikspezifizierungsprozess entwickelt: Zunächst wird eine Menge von Beispielmodellen erzeugt und deren erwartetes Verhalten formalisiert. Die DMM-Regeln werden dann inkrementell entwickelt, wobei geprüft wird, ob die Beispielmodelle tatsächlich das erwartete Verhalten erzeugen. Zudem haben wir Abdeckungskriterien für Tests von DMM-Spezifikationen entwickelt, die die Beurteilung der Qualität der Tests erlauben. Drittens haben wir gezeigt, wie funktionale und nichtfunktionale Anforderungen an Modelle und ihre DMM-Spezifikation formuliert und geprüft werden können. Für ersteres haben wir eine graphische Sprache zur Formulierung temporallogischer Eigenschaften zur Verfügung gestellt, die dann mit Model Checking geprüft werden. Für zweiteres ermöglichen wir dem Modellierer das Hinzufügen von Performanceinformationen zu den Modellen, aufgrund dessen dann z.B. der average throughput eines Modells berechnet werden kann.Dynamic Meta Modeling (DMM) is a semantics specification technique targeted at MOF-based modeling languages, where a language's behavior is defined by means of graphical operational rules which change runtime models. The DMM approach has first been suggested by Engels et al. in 2000; Hausmann has then defined the DMM language on a conceptual level within his PhD thesis in 2006. Consequently, the next step was to bring the existing DMM concepts alive, and then to apply them to different modeling languages, making use of the lessons learned to improve the DMM concepts as well as the DMM tooling. The result of this process is the DMM++ method, which is presented within this thesis. Our contributions are three-fold: First, and according to our experiences with the DMM language, we have introduced new concepts such as refinement by means of rule overriding, and we have strengthened existing concepts such as the dealing with universal quantified structures or attributes. Second, we have developed a test-driven process for semantics specification: A set of test models is created, and their expected behavior is fixed. Then, the DMM rules are created incrementally, finally resulting in a DMM ruleset realizing at least the expected behavior of the test models. Additionally, we have defined a set of coverage criteria for DMM rulesets which allow to measure the quality of a set of test models. Third, we have shown how functional as well as non-functional requirements can be formulated against models and their DMM specifications. The former is achieved by providing a visual language for formulating temporal logic properties, which are then verified with model checking techniques, and by allowing for visual debugging of models failing a requirement. For the latter, the modeler can add performance information to models and analyze their performance properties, e.g. average throughput.Tag der Verteidigung: 04.07.2013Paderborn, Univ., Diss., 201

SDK development for bridging heterogeneous data sources through connect bridge platform

Nesta dissertação apresentou-se um SDK para a criação de conectores a integrar com o CB Server, que pretende: acelerar o desenvolvimento, garantir melhores práticas e simplificar as diversas atividades e tarefas no processo de desenvolvimento. O SDK fornece uma API pública e simples, suportada por um conjunto de ferramentas, que facilitam o processo de desenvolvimento, explorando as facilidades disponibilizadas através da API. Para analisar a exatidão, viabilidade, integridade e acessibilidade da solução apresentam-se dois exemplos e casos de estudo. Através dos casos de estudo foi possível identificar uma lista de problemas, de pontos sensíveis e melhorias na solução proposta. Para avaliar a usabilidade da API, uma metodologia baseada em vários métodos de avaliação de usabilidade foi estabelecida. O múltiplo caso de estudo funciona como o principal método de avaliação, combinando vários métodos de pesquisa. O caso de estudo consiste em três fases de avaliação: um workshop, uma avaliação heurística e uma análise subjetiva. O caso de estudo envolveu três engenheiros de software (incluindo programadores e avaliadores). A metodologia aplicada gerou resultados com base num método de inspeção, testes de utilizador e entrevistas. Identificou-se não só pontos sensíveis e falhas no código-fonte, mas também problemas estruturais, de documentação e em tempo de execução, bem como problemas relacionados com a experiência do utilizador. O contexto do estudo é apresentado de modo a tirar conclusões acerca dos resultados obtidos. O trabalho futuro incluirá o desenvolvimento de novas funcionalidades. Adicionalmente, pretende-se resolver problemas encontrados na metodologia aplicada para avaliar a usabilidade da API, nomeadamente problemas e falhas no código fonte (por exemplo, validações) e problemas estruturais.In this dissertation, we present an SDK for the creation of connectors to integrate with CB Server which accelerates deployment, ensures best practices and simplifies the various activities and tasks in the development process. The SDK provides a public and simple API leveraged by a set of tools around the API developed which facilitate the development process by exploiting the API facilities. To analyse the correctness, feasibility, completeness, and accessibility of our solution, we presented two examples and case studies. From the case studies, we derived a list of issues found in our solution and a set of proposals for improvement. To evaluate the usability of the API, a methodology based on several usability evaluation methods has been established. Multiple case study works as the main evaluation method, combining several research methods. The case study consists of three evaluation phases – a hands-on workshop, a heuristic evaluation and subjective analysis. The case study involved three computer science engineers (including novice and expert developers and evaluators). The applied methodology generated insights based on an inspection method, a user test, and interviews. We identify not only problems and flaws in the source code, but also runtime, structural and documentation problems, as well as problems related to user experience. To help us draw conclusion from the results, we point out the context of the study. Future work will include the development of new functionalities. Additionally, we aim to solve problems found in the applied methodology to evaluate the usability of the API, namely problems and flaws in the source code (e.g. validations) and structural problems