arXiv subjects

Bernhard Rumpe

Publications and source records attributed to Bernhard Rumpe.

At least 19 recordsLinked to original sources

arXiv2025

Building Whitespace-Sensitive Languages Using Whitespace-Insensitive Components

In Software Language Engineering, there is a trend towards reusability by composing modular language components. However, this reusability is severely inhibited by a gap in integrating whitespace-sensitive and whitespace-insensitive languages. There is currently no consistent procedure for seamlessly reusing such language components in both cases, such that libraries often cannot be reused, and whitespacesensitive languages are developed from scratch. This paper presents a technique for using modular, whitespaceinsensitive language modules to construct whitespace sensitive languages by pre-processing language artifacts before parsing. The approach is evaluated by reconstructing a simplified version of the programming language Python. Our solution aims to increase the reusability of existing language components to reduce development time and increase the overall quality of software languages.

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arXiv2025

Addressing Visual Impairments with Model-Driven Engineering: A Systematic Literature Review

Software applications often pose barriers for users with accessibility needs, e.g., visual impairments. Model-driven engineering (MDE), with its systematic nature of code derivation, offers systematic methods to integrate accessibility concerns into software development while reducing manual effort. This paper presents a systematic literature review on how MDE addresses accessibility for vision impairments. From 447 initially identified papers, 30 primary studies met the inclusion criteria. About two-thirds reference the Web Content Accessibility Guidelines (WCAG), yet their project-specific adaptions and end-user validations hinder wider adoption in MDE. The analyzed studies model user interface structures, interaction and navigation, user capabilities, requirements, and context information. However, only few specify concrete modeling techniques on how to incorporate accessibility needs or demonstrate fully functional systems. Insufficient details on MDE methods, i.e., transformation rules or code templates, hinder the reuse, generalizability, and reproducibility. Furthermore, limited involvement of affected users and limited developer expertise in accessibility contribute to weak empirical validation. Overall, the findings indicate that current MDE research insufficiently supports vision-related accessibility. Our paper concludes with a research agenda outlining how support for vision impairments can be more effectively embedded in MDE processes.

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arXiv2025

Tool-Assisted Conformance Checking to Reference Process Models

Reference models convey best practices and standards. The reference frameworks necessitate conformance checks to ensure adherence to established guidelines and principles, which is crucial for maintaining quality and consistency in various processes. This paper explores automated conformance checks for concrete process models against reference models using causal dependency analysis of tasks and events. Existing notions of conformance checking for process models focus on verifying process execution traces and lack the expressiveness and automation needed for semantic model comparison, leaving this question unresolved. We integrate our approach into a broader semantic framework for defining reference model conformance. We outline an algorithm for reference process model conformance checking, evaluate it through a case study, and discuss its strengths and limitations. Our research provides a tool-assisted solution enhancing accuracy and flexibility in process model conformance verification.

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arXiv2025

Dynamic Symbolic Execution for Semantic Difference Analysis of Component and Connector Architectures

In the context of model-driven development, ensuring the correctness and consistency of evolving models is paramount. This paper investigates the application of Dynamic Symbolic Execution (DSE) for semantic difference analysis of component-and-connector architectures, specifically utilizing MontiArc models. We have enhanced the existing MontiArc-to-Java generator to gather both symbolic and concrete execution data at runtime, encompassing transition conditions, visited states, and internal variables of automata. This data facilitates the identification of significant execution traces that provide critical insights into system behavior. We evaluate various execution strategies based on the criteria of runtime efficiency, minimality, and completeness, establishing a framework for assessing the applicability of DSE in semantic difference analysis. Our findings indicate that while DSE shows promise for analyzing component and connector architectures, scalability remains a primary limitation, suggesting further research is needed to enhance its practical utility in larger systems.

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arXiv2025

Towards a Unifying Reference Model for Digital Twins of Cyber-Physical Systems

Digital twins are sophisticated software systems for the representation, monitoring, and control of cyber-physical systems, including automotive, avionics, smart manufacturing, and many more. Existing definitions and reference models of digital twins are overly abstract, impeding their comprehensive understanding and implementation guidance. Consequently, a significant gap emerges between abstract concepts and their industrial implementations. We analyze popular reference models for digital twins and combine these into a significantly detailed unifying reference model for digital twins that reduces the concept-implementation gap to facilitate their engineering in industrial practice. This enhances the understanding of the concepts of digital twins and their relationships and guides developers to implement digital twins effectively.

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arXiv2025

Overcoming the hurdle of legal expertise: A reusable model for smartwatch privacy policies

Regulations for privacy protection aim to protect individuals from the unauthorized storage, processing, and transfer of their personal data but oftentimes fail in providing helpful support for understanding these regulations. To better communicate privacy policies for smartwatches, we need an in-depth understanding of their concepts and provide better ways to enable developers to integrate them when engineering systems. Up to now, no conceptual model exists covering privacy statements from different smartwatch manufacturers that is reusable for developers. This paper introduces such a conceptual model for privacy policies of smartwatches and shows its use in a model-driven software engineering approach to create a platform for data visualization of wearable privacy policies from different smartwatch manufacturers. We have analyzed the privacy policies of various manufacturers and extracted the relevant concepts. Moreover, we have checked the model with lawyers for its correctness, instantiated it with concrete data, and used it in a model-driven software engineering approach to create a platform for data visualization. This reusable privacy policy model can enable developers to easily represent privacy policies in their systems. This provides a foundation for more structured and understandable privacy policies which, in the long run, can increase the data sovereignty of application users.

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arXiv2024

Using Grammar Masking to Ensure Syntactic Validity in LLM-based Modeling Tasks

We present and evaluate a method called grammar masking, which is used to guide large language models (LLMs) toward producing syntactically correct models for a given context-free grammar. Prompt engineering methods such as few-shot learning or priming can be used to improve the chances of an LLM producing correct syntax, but the more complex the grammar, the more time-consuming and less promising these methods become. Previous work is focused primarily on the usage of either language model training or prompt engineering. In this work, a method is presented that restricts the output to a given grammar using constrained decoding to ensure the output adheres to a valid syntax. We use several DSLs built with MontiCore and task multiple LLMs to produce models with and without constrained decoding. A corresponding parser is used to confirm the syntactic correctness of each model. We show that grammar masking can dramatically improve the modeling capabilities of several LLMs, reducing the need for well-refined prompting while increasing the chance of producing correct models.

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arXiv2024

Toward Research Software Categories

Research software has been categorized in different contexts to serve different goals. We start with a look at what research software is, before we discuss the purpose of research software categories. We propose a multi-dimensional categorization of research software. We present a template for characterizing such categories. As selected dimensions, we present our proposed role-based, developer-based, and maturity-based categories. Since our work has been inspired by various previous efforts to categorize research software, we discuss them as related works. We characterize all these categories via the previously introduced template, to enable a systematic comparison.

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arXiv2023

Context, Composition, Automation, and Communication -- The C2AC Roadmap for Modeling and Simulation

Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressing these limitations. The first is to provide better support for capturing, representing, and evaluating the context of simulation studies, including research questions, assumptions, requirements, and activities contributing to a simulation study. In addition, the composition of simulation models and other simulation studies' products must be supported beyond syntactical coherence, including aspects of semantics and purpose, enabling their effective reuse. A higher degree of automating simulation studies will contribute to more systematic, standardized simulation studies and their efficiency. Finally, it is essential to invest increased effort into effectively communicating results and the processes involved in simulation studies to enable their use in research and decision-making. These goals are not pursued independently of each other, but they will benefit from and sometimes even rely on advances in other subfields. In the present paper, we explore the basis and interdependencies evident in current research and practice and delineate future research directions based on these considerations.

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arXiv2022

Technical Report on Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE

Systems engineering, in particular in the automotive domain, needs to cope with the massively increasing numbers of requirements that arise during the development process. To guarantee a high product quality and make sure that functional safety standards such as ISO26262 are fulfilled, the exploitation of potentials of model-driven systems engineering in the form of automatic analyses, consistency checks, and tracing mechanisms is indispensable. However, the language in which requirements are written, and the tools needed to operate on them, are highly individual and require domain-specific tailoring. This hinders automated processing of requirements as well as the linking of requirements to models. Introducing formal requirement notations in existing projects leads to the challenge of translating masses of requirements and process changes on the one hand and to the necessity of the corresponding training for the requirements engineers. In this paper, based on the analysis of an open-source set of automotive requirements, we derive domain-specific language constructs helping us to avoid ambiguities in requirements and increase the level of formality. The main contribution is the adoption and evaluation of few-shot learning with large pretrained language models for the automated translation of informal requirements to structured languages such as a requirement DSL. We show that support sets of less than ten translation examples can suffice to few-shot train a language model to incorporate keywords and implement syntactic rules into informal natural language requirements.

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arXiv2022

Design Guidelines for Improving User Experience in Industrial Domain-Specific Modelling Languages

Domain-specific modelling languages (DSMLs) help practitioners solve modelling challenges specific to various domains. As domains grow more complex and heterogeneous in nature, industrial practitioners often face challenges in the usability of graphical DSMLs. There is still a lack of guidelines that industrial language engineers should consider for improving the user experience (UX) of these practitioners. The overall topic of UX is vast and subjective, and general guidelines and definitions of UX are often overly generic or tied to specific technological spaces. To solve this challenge, we leverage existing design principles and standards of human-centred design and UX in general and propose definitions and guidelines for UX and user experience design (UXD) aspects in graphical DSMLs. In this paper, we categorize the key UXD aspects, primarily based on our experience in developing industrial DSMLs, that language engineers should consider during graphical DSML development. Ultimately, these UXD guidelines help to improve the general usability of industrial DSMLs and support language engineers in developing better DSMLs that are independent of graphical modelling tools and more widely accepted by their users.

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arXiv2022

MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat

In this paper, we propose to adopt the MDE paradigm for the development of Machine Learning (ML)-enabled software systems with a focus on the Internet of Things (IoT) domain. We illustrate how two state-of-the-art open-source modeling tools, namely MontiAnna and ML-Quadrat can be used for this purpose as demonstrated through a case study. The case study illustrates using ML, in particular deep Artificial Neural Networks (ANNs), for automated image recognition of handwritten digits using the MNIST reference dataset, and integrating the machine learning components into an IoT system. Subsequently, we conduct a functional comparison of the two frameworks, setting out an analysis base to include a broad range of design considerations, such as the problem domain, methods for the ML integration into larger systems, and supported ML methods, as well as topics of recent intense interest to the ML community, such as AutoML and MLOps. Accordingly, this paper is focused on elucidating the potential of the MDE approach in the ML domain. This supports the ML engineer in developing the (ML/software) model rather than implementing the code, and additionally enforces reusability and modularity of the design through enabling the out-of-the-box integration of ML functionality as a component of the IoT or cyber-physical systems.

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arXiv2021

Self-Adaptive Manufacturing with Digital Twins

Digital Twins are part of the vision of Industry 4.0 to represent, control, predict, and optimize the behavior of Cyber-Physical Production Systems (CPPSs). These CPPSs are long-living complex systems deployed to and configured for diverse environments. Due to specific deployment, configuration, wear and tear, or other environmental effects, their behavior might diverge from the intended behavior over time. Properly adapting the configuration of CPPSs then relies on the expertise of human operators. Digital Twins (DTs) that reify this expertise and learn from it to address unforeseen challenges can significantly facilitate self-adaptive manufacturing where experience is very specific and, hence, insufficient to employ deep learning techniques. We leverage the explicit modeling of domain expertise through case-based reasoning to improve the capabilities of Digital Twins for adapting to such situations. To this effect, we present a modeling framework for self-adaptive manufacturing that supports modeling domain-specific cases, describing rules for case similarity and case-based reasoning within a modular Digital Twin. Automatically configuring Digital Twins based on explicitly modeled domain expertise can improve manufacturing times, reduce wastage, and, ultimately, contribute to better sustainable manufacturing.

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arXiv2021

Towards a Systematic Engineering of Industrial Domain-Specific Language

Domain-Specific Languages (DSLs) help practitioners in contributing solutions to challenges of specific domains. The efficient development of user-friendly DSLs suitable for industrial practitioners with little expertise in modelling still is challenging. For such practitioners, who often do not model on a daily basis, there is a need to foster reduction of repetitive modelling tasks and providing simplified visual representations of DSL parts. For industrial language engineers, there is no methodical support for providing such guidelines or documentation as part of reusable language modules. Previous research either addresses the reuse of languages or guidelines for modelling. For the efficient industrial deployment of DSLs, their combination is essential: the efficient engineering of DSLs from reusable modules that feature integrated documentation and guidelines for industrial practitioners. To solve these challenges, we propose a systematic approach for the industrial engineering of DSLs based on the concept of reusable DSL Building Blocks, which rests on several years of experience in the industrial engineering of DSLs and their deployment to various organizations. We investigated our approach via focus group methods consisting of five participants from industry and research qualitatively. Ultimately, DSL Building Blocks support industrial language engineers in developing better usable DSLs and industrial practitioners in more efficiently achieving their modelling.

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arXiv2020

Software Engineering und Software Engineering Forschung im Zeitalter der Digitalisierung

Digitization not only affects society, it also requires a redefinition of the location of computer science and computer scientists, as the science journalist Yogeshwar suggests. Since all official aspects of digitalization are based on software, this article is intended to attempt to redefine the role of software engineering and its research. Software-based products, systems or services are influencing all areas of life and are a critical component and central innovation driver of digitization in all areas of life. Scientifically, there are new opportunities and challenges for software engineering as a driving discipline in the development of any technical innovation. However, the chances must not be sacrificed to the competition for bibliometric numbers as an end in themselves.

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arXiv2016

Adaptable Symbol Table Management by Meta Modeling and Generation of Symbol Table Infrastructures

Many textual software languages share common concepts such as defining and referencing elements, hierarchical structures constraining the visibility of names, and allowing for identical names for different element kinds. Symbol tables are useful to handle those reference and visibility concepts. However, developing a symbol table can be a tedious task that leads to an additional effort for the language engineer. This paper presents a symbol table meta model usable to define languagespecific symbol tables. Furthermore, we integrate this symbol table meta model with a meta model of a grammar-based language definition. This enables the language engineer to switch between the model structure and the symbol table as needed. Finally, based on a grammarannotation mechanism, our approach is able to generate a symbol table infrastructure that can be used as is or serve as a basis for custom symbol tables.

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arXiv2016

Engineering Tagging Languages for DSLs

To keep a DSL clean, readable and reusable in different contexts, it is useful to define a separate tagging language. A tag model logically adds information to the tagged DSL model while technically keeping the artifacts separated. Using a generic tagging language leads to promiscuous tag models, whereas defining a target DSL-specific tag language has a high initial overhead. This paper presents a systematic approach to define a DSL-specific tag language and a corresponding schema language, combining the advantages of both worlds: (a) the tag language specifically fits to the DSL, (b) the artifacts are kept separated and enabling reuse with different tag decorations, (c) the tag language follows a defined type schema, and (d) systematic derivation considerably reduces the effort necessary to implement the tag language. An example shows that it can at least partially be realized by a generator and applied for any kind of DSL. Index Terms Software Engineering, Modeling, MDE, GSE

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arXiv2016

TUnit - Unit Testing For Template-based Code Generators

Template-based code generator development as part of model-drivendevelopment (MDD) demands for strong mechanisms and tools that support developers to improve robustness, i.e., the desired code is generated for the specified inputs. Although different testing methods have been proposed,a method for testing only parts of template-based code generators that can be employed in the early stage of development is lacking. Thus, in this paper we present an approach and an implementation based on JUnit to test template-based code generators. Rather than testing a complete code generator,it facilitates partial testing by supporting the execution of templates with a mocked environment. This eases testing of code generators in early stages of development as well as testing new orchanged parts of a code generator. To test the source code generated by the templates under test, different methods are presented including string comparisons, API-based assertions, and abstract syntax tree based assertions.

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