Constructing Dependability Analysis Models of Reconfigurable Production Systems

Compared to General Purpose Modeling Languages (GPMLs), Domain Specific Modeling Languages (DSMLs) have brought increasing expressivity and conciseness through the use of concepts and notations, which are customized by the characteristics of a specific problem domain. However, the creation of completely new languages can significantly increase software development costs. An alternative approach to creating new DSMLs refers to a customization of existing modeling languages. The DSML creation process through a customization of existing languages is a special case of Language Embedding. The embedding provides customization of an inherited infrastructure to a given domain. This paper elaborates a Domain-Specific Modeling environment for embedding an arbitrary DSML into an arbitrary GPML. The environment enables uniform development of DSMLs in the form of a lightweight extension of existing modeling languages.

General-purpose modeling languages are inadequate to model and visualize business processes precisely. An enterprise has its own vocabulary for modeling processes and its specific tasks may have attached data that define the tasks precisely. We propose using Domain Specific Modeling (DSM) languages to model business processes, such that an enterprise can define its own DSM language(s) capturing its vocabulary and data requirement. We suggest using UML profiles and UML activity diagrams as the semantic base for these DSM languages and present tools that are able to create a DSM language and tool support for a given domain. One tool, called ADSpecializer, can generate a UML profile and its tool support of a given application domain. The other tool, ADModeler, is used to create UML activity diagrams within such a domain-specific UML profile. The two tools enable an enterprise to efficiently define and utilize their own DSM language.

The International Workshop on Non-functional System Properties in Domain Specific Modeling Languages (NFPinDSML) series traditionally takes place as part of the Satellite Events of the ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS). Traditionally, NFPinDSML gathers researchers and practitioners interested in the estimation and evaluation of system quality and their integration in Domain Specific Modeling Languages and Model Driven Engineering in general. This paper is the summary of the fourth NFPinDSML workshop which was affiliated with MODELS 2012.

Domain-specific modelling langugages, which are tailored to the requirements of their users, can significantly increase the acceptance of formal (or at least semi-formal) modelling in scenarios where informal diagrams and natural language descriptions are predominant today. We show in this article how the Resource Description Framework (RDF), which is a standard for the fundamental data structures of the Semantic Web, and algebraic graph transformations on these data structures can be used to realise and modify the abstract syntax of models in such domain-specific languages. We examine a small domain-specific modelling language for IT infrastructures--inspired by real-world requirements from a banking environment--as an application scenario. From this scenario, we derive four key requirements for a domain-specific modelling framework: (1) distributed modelling, (2) evolution of language definitions, (3) migration of legacy models and (4) integration of modelling languages. RDF and transformation rules are then used to provide a solution which meets these requirements simultaneously, where all kinds of modifications--from simple editing steps via model migration to language integration--are realised in an integrated manner by the single, declarative formalism of algebraic graph transformation.

The subject of the research is machine learning models and methods for generating legal contracts with limited resources and performance evaluation benchmarks. The goal of the work is to analyse approaches of domain-specific Large Language Models development and to find the optimal method of creating independent specialized systems that can generate contracts in different languages and legal systems. The article addresses the following tasks: identification of existing companies and solutions in this area, exploring approaches to create texts in natural language, analysis of evaluation and comparison methods of such systems, inspecting limitations and shortcomings of existing solutions and approaches, finding the optimal method of developing systems with limited resources. The following results were obtained: approaches of natural language generation and their features were investigated; the "Transformer" architecture was defined as a modern standard in the field of text information generation; different model types which are based on this architecture were considered; data sources for training were analysed; methods of adapting models in specialized areas were considered; model evaluating benchmarks for various tasks were reviewed; shortcomings of the existing specialized language models and the incompleteness of existing benchmarks for contract generation task evaluation were revealed. As a result of the analytical experiment, it was determined that the Retrieval-Augmented Generation method is the most optimal for solving the given task under the given conditions. The conducted experiment and its results can be used as a basis for further research of domain-specific language models development with limited resources. Conclusions: the article provides an overview of natural language generation methods using modern machine learning techniques, considers their advantages and disadvantages for small companies and scientific institutions that have limited resources. The work examinates a specialized legal domain and the problem of contract generation and determines the most optimal method to solve it.

Software development processes are collaborative in nature. Neglecting the key role of end-users leads to software unlikely to satisfy their needs. This collaboration becomes specially important when creating Domain-Specific Modeling Languages (DSMLs), which are (modeling) languages specifically designed to carry out the tasks of a particular domain. While end-users are actually the experts of the domain for which a DSML is developed, their participation in the DSML specification process is still rather limited nowadays. In this paper, we propose a more community-aware language development process by enabling the active participation of all community members (both developers and end-users of the DSML) from the very beginning. Our proposal is based on a DSML itself, called Collaboro, which allows representing change proposals on the DSML design and discussing (and tracing back) possible solutions, comments and decisions arisen during the collaboration. Collaboro also incorporates a metric-based recommender system to help community members to define high-quality notations for the DSMLs. We also show how Collaboro can be used at the model-level to facilitate the collaborative specification of software models.

The development and use of Domain Specific Languages emerged as a way to cope with complex problems using concepts closer to the problem domain. By leveraging the principles proposed by Model-Driven Development (MDD), like the separation of concerns and the use of model transformations, this approach became popular and caused the emergence of a variety of languages, known as Domain Specific Modeling Languages (DSMLs). Moreover, the use of DSMLs with graphical notations abstracts even more the problem domain, either by using extensions of UML or directly using metamodeling languages. The definition and use of DSMLs is only possible through specific tools, known as languages workbenches. This paper discusses the analysis and comparison of three of these tools (namely Papyrus, Enterprise Architect and Sirius) that were used to create the XIS-Mobile language, a non-trivial DSML defined as a UML profile for modeling mobile applications in a platform-independent way. These tools were evaluated considering a set of key criteria (namely learnability, usability, graphical completeness, validation support, transformation support, evolvability and interoperability) which show their suitability to develop non-trivial languages.

Domain-specific modeling languages (DSMLs) facilitate concise and succinct model descriptions. DSMLs are commonly realized by defining a custom grammar and executing models in an interpreter. This provides flexibility in language design as well as in the implementation of simulation algorithms. However, this type of implementation can lead to a negative impact on simulation performance in comparison to implementing models in general-purpose programming languages (GPL). To mitigate this problem, we propose using runtime code generation. This allows us to replace specific parts of a model at runtime by compiling generated GPL source code. In this paper, we demonstrate the potential benefit of this concept based on ML-Rules, a DSML for modeling and simulating biochemical reaction networks. Using code generation for arithmetic expressions in ML-Rules' reaction rate functions, we were able to decrease the runtime by up to 40% in complex application scenarios.

Frameworks support application development improving productivity and quality. However, due to the number of hot stops to configure them, reusing frameworks may be a complex task which can make developers misuse them and insert defects in the application code. In order to reduce the complexity of this task, we propose increase its abstraction level by using Domain-Specific Modeling Languages (DSML) based on framework features to model and to generate applications that reuse these frameworks. In our approach, framework features and their hot spots are identified from an analysis of its source code and documentation, and then a DSML and a set of templates are constructed. With this DSML, applications reusing the framework can be modeled and generated, protecting developers from framework complexity and decreasing the time spent on the implementation. We illustrate our approach using GRENJ framework as example.KeywordsFrameworkFeatureDomain-specific modeling languageTemplateCode generation

The emergence of modern concurrent systems (e.g., Cyber- Physical Systems or the Internet of Things) and highly- parallel platforms (e.g., many-core, GPGPU pipelines, and distributed platforms) calls for Domain-Specific Modeling Languages (DSMLs) where concurrency is of paramount im- portance. Such DSMLs are intended to propose constructs with rich concurrency semantics, which allow system design- ers to precisely define and analyze system behaviors. How- ever, specifying and implementing the execution semantics of such DSMLs can be a difficult, costly and error-prone task. Most of the time the concurrency model remains implicit and ad-hoc, embedded in the underlying execution environ- ment. The lack of an explicit concurrency model prevents: the precise definition, the variation and the complete under- standing of the semantics of the DSML, the effective usage of concurrency-aware analysis techniques, and the exploitation of the concurrency model during the system refinement (e.g., during its allocation on a specific platform). In this paper, we introduce a concurrent executable metamodeling approach, which supports a modular definition of the execution seman- tics, including the concurrency model, the semantic rules, and a well-defined and expressive communication protocol between them. Our approach comes with a dedicated meta- language to specify the communication protocol, and with an execution environment to simulate executable models. We illustrate and validate our approach with an implementation of fUML, and discuss the modularity and applicability of our approach.

ADELFE is a French acronym that means “Toolkit for Designing Software with Emergent Functionalities” (“Atelier de DEveloppement de Logiciels à Fonctionnalité Emergente” in French). ADELFE methodology is dedicated to applications characterized by openness and the need of the system adaptation to an environment. Its main goal is to help and guide any designer during the development of an Adaptive Multi-agent System (AMAS). An AMAS is characterized by the following points: it is plunged into an environment and composed of interdependent agents, each agent carries out a partial function and the agents, organization during runtime makes the system realize an emergent function. Actually, an agent is locally cooperative, i.e. it is able to recognize cooperation failures called Non-cooperative Situations (NCS, which could be related to exceptions in classical programs) and treat them.ADELFE includes five Work Definitions that were initially inspired from the Rational Unified Process (RUP) and gathers 21 activities, producing or refining 12 work products. These products are aggregating modelling diagrams or structured or free text. ADELFE, which is a Model-Driven (model-centred) development method, is not hardly dependent on Domain Specific Modelling Languages (DSML) but currently the recommendation is to use UML2 for general activities and to use AMASML (AMAS Modelling Language) and SpeADL (Species-based Modelling Language) for specific activities appearing in Analysis, Design or Implementation phases.KeywordsAdaptive Multi-agent Systems (AMAS)Domain-specific Modeling Languages (DSML)AMAS ApproachAMAS TheoryManagement Studies ConferenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Succinct, declarative, and domain-specific modeling languages have many advantages when creating simulation models. However, it is often challenging to efficiently execute models defined in such languages. We use code generation for model-specific simulators. Code generation has been successfully applied for high-performance algorithms in many application domains. By generating tailored simulators for specific simulation models defined in a domain specific language, we get the best of both worlds: a succinct, declarative and formal presentation of the model and an efficient execution. We illustrate this based on a simple domain-specific language for biochemical reaction networks as well as on the network representation of the established BioNetGen language. We implement two approaches adopting the same simulation algorithms: one generic simulator that parses models at runtime and one generator that produces a simulator specialized to a given model based on partial evaluation and code generation. Akin to profile-guided optimization we also use dynamic execution of the model to further optimize the simulators. The performance of the approaches is carefully benchmarked using representative models of small to mid-sized biochemical reaction networks. The generic simulator achieves a performance similar to state of the art simulators in the domain, whereas the specialized simulator outperforms established simulation algorithms with a speedup of more than an order of magnitude. This repository contains the code generation software as described in the 2020 PADS paper Partial evaluation via code generation for static stochastic reaction network models.

Domain-Specific Modeling Languages to improve framework instantiation

As Domain-Specific Modeling Languages (DSML) become more widespread, it is now possible for non-computer scientists to design complex systems. Not only do DSMLs make modeling more accessible to domain experts, they also improve their efficiency. Using such models, the problem of their maintenance and evolution arises, with common software engineering issues, such as reversibility and traceability of developments. In this article we present the integration of a collaborative edition process into a model-driven engineering (MDE) simulation platform defining its own DSML, which includes a serialization procedure. Our novel approach defines a collaborative work and version control process. This process uses the concrete textual syntax defined by this DSML, which allows us to use the features of existing Version Control Systems (VCS) to work with serialized models, and helps calculate the differences between two versions of a model, merge versions of these models, and detect merge conflicts.

Multilevel Modelling (MLM) approaches make it possible for designers and modellers to work with an unlimited number of abstraction levels to specify their domain-specific modelling languages (DSMLs). To fully exploit MLM techniques, we need powerful model composition operators. Indeed, the composition of DSMLs is becoming increasingly relevant to the modelling community either because some DSMLs may share commonalities that we want to make reusable, or because we want to facilitate interoperability between DSMLs. In this paper, we propose a composition mechanism for structure and behaviour of multilevel modelling hierarchies. Our approach facilitates the inclusion of additional features while keeping a clear separation of concerns that enhances modularity. We provide a formal semantics of the constructions based on category theory and graph transformations, and show their use in practice on a case study.