Software and Systems Modeling最新文献

With many governments regulating the handling of user data-the General Data Protection Regulation, the California Consumer Privacy Act, and the Saudi Arabian Personal Data Protection Law-ensuring systems comply with data privacy legislation is of high importance. Checking compliance is a tricky process and often includes many manual elements. We propose that formal methods, that model systems mathematically, can provide strong guarantees to help companies prove their adherence to legislation. To increase usability we advocate a diagrammatic approach, based on bigraphical reactive systems, where privacy experts can explicitly visualise the systems and describe updates, via rewrite rules, that describe system behaviour. The rewrite rules allow flexibility in integrating privacy policies with user-specified systems. We focus on modelling notions of providing consent, withdrawing consent, purpose limitations, the right to access and sharing data with third parties, and define privacy properties that we want to prove within the systems. Properties are expressed using the computation tree logic and proved using model checking. To show the generality of the proposed framework, we apply it to two examples: a bank notification system, inspired by Monzo's privacy policy, and a cloud-based home healthcare system based on the Fitbit app's privacy policy.

Setting up and configuring model-driven engineering (MDE) tools is not straightforward because the MDE tooling landscape is highly fragmented and because many MDE tools are research prototypes with limited documentation. This creates significant accidental complexity for learners of MDE, who have to overcome installation and configuration hurdles before they can even begin to focus on the core MDE concepts they should be learning. This is further complicated by the complexity of modern MDE tools, which can overwhelm new learners, making it difficult for them to work out what they should do next to achieve a given goal. To address these challenges, we have developed a web-based playground platform that enables learners to engage with MDE learning activities without the need to install anything. The playground metaphor allows teachers to expose only those functionalities directly required for the completion of a particular learning activity. We present the general architecture of the platform, our approach to the declarative integration of new MDE tools, and the way in which teachers can flexibly and declaratively define new MDE learning activities. We have used our platform in a range of different contexts, from live tutorials and 10-week university courses, to developing documentation webpages for MDE tools. We describe examples of such uses, showcasing the flexible configurability of the platform for different types of activities and contexts.

Simulation is a favoured technique in robotics. It is, however, costly, in terms of development time, and its usability is limited by the lack of standardisation and portability of simulators. We present RoboSim, a diagrammatic tool-independent domain-specific language to model robotic platforms and their controllers. It can be regarded as a profile of UML/SysML enriched with time primitives, differential equations, and a mathematical semantics. Our previous work on RoboSim described a notation to specify control software. In this paper, we present a novel notation to describe physical models: block diagrams that can be linked to the platform-independent software model to characterise how services required by the software are realised by actuators and sensors. Behaviours are specified by differential equations, and simulations and mathematical models of the whole system can be generated automatically. Our main contributions are a modular and extensible diagrammatic notation that supports the explicit specification of physical behaviours; a set of validation rules that identify well-formed models; a model-to-model transformation from RoboSim to an input format accepted by several simulators; and a formal semantics for mathematical reasoning.

In software engineering, models are used for many different things. In this paper, we focus on program verification, where we use models to reason about the correctness of systems. There are many different types of program verification techniques which provide different correctness guarantees. We investigate the domain of program verification tools and present a concise megamodel to distinguish these tools. We also present a data set of 400+ program verification tools. This data set includes the category of verification tool according to our megamodel, practical information such as input/output format, repository links and more. The practical information, such as last commit date, is kept up to date through the use of APIs. Moreover, part of the data extraction has been automated to make it easier to expand the data set. The categorisation enables software engineers to find suitable tools, investigate alternatives and compare tools. We also identify trends for each level in our megamodel. Our data set, publicly available at https://doi.org/10.4121/20347950, can be used by software engineers to enter the world of program verification and find a verification tool based on their requirements. This paper is an extended version of https://doi.org/10.1145/3550355.3552426.

Verifying safety requirements by model checking becomes increasingly important for safety-critical applications. For the validity of such proof in practice, the model needs to capture the actual behavior of the real system, which could be tested by containment checks of real observation traces. Basic equivalence checks, however, are not applicable if the system is only partially or imprecisely observable, if the model abstracts from explicit states with symbolic semantics, or if the checks are not expressible in the logics supported by a model checker. In this article, we solve the problem of observation containment checking in timed automata via reachability checking on tester systems. We introduce the logic SRL (sequence reachability logic) to express observations as sequences of delayed reachability properties. Through SBLL (introduced by Aceto et al.) as intermediate logic, we synthesize a set of matcher model templates for partial and imprecise observations and further extend these templates for the case of limited state accessibility in a model. For the obtained matching traces, we define the back-transformation into the original model domain and formally prove the correctness of the transformation. We implemented the observation matching approach, and apply it to a set of 7 demo and 3 case study models with different levels of observability. The results show that all positive and negative observations are correctly classified, and that the most advanced matcher model instance still offers average run times between 0.1 and 1 s in all but 3 scenarios.

The complexity of safety-critical systems is continuously increasing. To create safe systems despite the complexity, the system development requires a strong integration of system design and safety activities. A promising choice for integrating system design and safety activities are model-based approaches. They can help to handle complexity through abstraction, automation, and reuse and are applied to design, analyze, and assure systems. In practice, however, there is often a disconnect between the model-based design and safety activities. At the same time, there is often a delay until recent approaches are available in model-based frameworks. As a result, the advantages of the models are often not fully utilized. Therefore, this article proposes a framework that integrates recent approaches for system design (model-based systems engineering), safety analysis (system-theoretic process analysis), and safety assurance (goal structuring notation). The framework is implemented in the systems modeling language (SysML), and the focus is placed on the connection between the safety analysis and safety assurance activities. It is shown how the model-based integration enables tool assistance for the systematic creation, analysis, and maintenance of safety artifacts. The framework is demonstrated with the system design, safety analysis, and safety assurance of a collision avoidance system for aircraft. The model-based nature of the design and safety activities is utilized to support the systematic generation, analysis, and maintenance of safety artifacts.

Method engineering emerged in the 1990s as a discipline aiming to design, construct, and adapt methods, techniques, and tools for the development of information systems. By executing a method step by step, users can follow a well-defined process to achieve the intended results for which the method was created. To create methods in a more guided and systematic manner, a framework of methods can serve as a template. This allows individuals to leverage the expertise of method engineers who have consolidated their best practices within these frameworks. However, the creation and adoption of a method can be challenging in the absence of tools to support these activities. Additionally, method engineers may lack the programming skills required to implement such tools. In this context, we extend an approach inspired by the low-code paradigm for method engineering. By integrating construction rules for guidance (called here protocols), the goal of this approach is to assist method engineers in creating new methods or adapting existing frameworks. It automatically provides tool support, enabling method experts to effectively execute the method. This paper builds upon previous work and presents the approach through a proof-of-concept implementation, LOMET. We present a second version of LOMET, which has been refined based on feedback received during an empirical evaluation conducted through semi-structured interviews.

Language technologies are gaining momentum as textual information saturates social networks and media outlets, compounded by the growing role of fake news and disinformation. In this context, approaches to represent and analyse public speeches, news releases, social media posts and other types of discourses are becoming crucial. Although there is a large body of literature on text-based machine learning, it tends to focus on lexical and syntactical issues rather than semantic or pragmatic. Being useful, these advances cannot tackle the nuanced and highly context-dependent problems of discourse evaluation that society demands. In this paper, we present IAT/ML, a metamodel and modelling approach to represent and analyse discourses. IAT/ML focuses on semantic and pragmatic issues, thus tackling a little researched area in language technologies. It does so by combining three different modelling approaches: ontological, which focuses on what the discourse is about; argumentation, which deals with how the text justifies what it says; and agency, which provides insights into the speakers’ beliefs, desires and intentions. Together, these three modelling approaches make IAT/ML a comprehensive solution to represent and analyse complex discourses towards their understanding, evaluation and fact checking.

The paper proposes universal conceptual modeling, conceptual modeling that strives to be as general-purpose as possible and accessible to anyone, professionals and non-experts alike. The idea of universal conceptual modeling is meant to catalyze new thinking in conceptual modeling and be used to evaluate and develop conceptual modeling solutions, such as modeling languages, approaches for requirements elicitation, or modeling tools. These modeling solutions should be usable by as many people and design agents as possible and for as many purposes as possible, aspiring to the ideals of universal conceptual modeling. We propose foundations of universal conceptual modeling in the form of six principles: flexibility, accessibility, ubiquity, minimalism, primitivism, and modularity. We then demonstrate the utility of these principles to evaluate existing conceptual modeling languages and understand conceptual modeling practices. Finally, we propose future research opportunities meant to realize the ideals of universal conceptual modeling.

The integration of Large Language Models (LLMs) in software modeling tasks presents both opportunities and challenges. This Expert Voice addresses a significant gap in the evaluation of these models, advocating for the need for standardized benchmarking frameworks. Recognizing the potential variability in prompt strategies, LLM outputs, and solution space, we propose a conceptual framework to assess their quality in software model generation. This framework aims to pave the way for standardization of the benchmarking process, ensuring consistent and objective evaluation of LLMs in software modeling. Our conceptual framework is illustrated using UML class diagrams as a running example.