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Auflistung nach Schlagwort "Model-Driven Software Engineering"

1 - 6 von 6
  • Konferenzbeitrag
    Integrated Revision and Variation Control for Evolving Model-Driven Software Product Lines
    (Software Engineering 2020, 2020) Schwägerl, Felix; Westfechtel, Bernhard
    Software engineering projects are faced with abstraction, which is achieved by software models, historical evolution, which is addressed by revision control, and variability, which is managed with the help of software product line engineering. Addressing these phenomena by separate tools ignores obvious overlaps and therefore fails at exploiting synergies between revision and variation control for models. In this article, we present a conceptual framework for integrated revision and variation control of model-driven software projects and its implementation in the tool SuperMod.
  • Konferenzbeitrag
    Model-Driven Allocation Engineering – Abridged Version
    (Software Engineering 2017, 2017) Pohlmann, Uwe; Hüwe, Marcus
  • ConferencePaper
    MoFuzz: A Fuzzer Suite for Testing Model-Driven Software Engineering Tools
    (Software Engineering 2021, 2021) Nguyen, Hoang Lam; Nassar, Nebras; Kehrer, Timo; Grunske, Lars
    Fuzzing or fuzz testing is an established technique that aims to discover unexpected program behavior (\eg, bugs, vulnerabilities, or crashes) by feeding automatically generated data into a program under test. However, the application of fuzzing to test Model-Driven Software Engineering (MDSE) tools is still limited because of the difficulty of existing fuzzers to provide structured, well-typed inputs, namely models that conform to typing and consistency constraints induced by a given meta-model and underlying modeling framework. We present three different approaches for fuzzing MDSE tools: A graph grammar-based fuzzer and two variants of a coverage-guided mutation-based fuzzer working with different sets of model mutation operators. Our evaluation on a set of real-world MDSE tools shows that our approaches can outperform both standard fuzzers and model generators w.r.t. their fuzzing capabilities. Moreover, we found that each of our approaches comes with its own strengths and weaknesses in terms of code coverage and fault finding capabilities, thus complementing each other, forming a fuzzer suite for testing MDSE tools.
  • Konferenzbeitrag
    Monitoring the Execution of Declarative Model Transformations
    (Softwaretechnik-Trends Band 39, Heft 3, 2019) Groner, Raffaela; Gylstorff, Sophie; Tichy, Matthias
    Model transformations, applied at design and run time, are key artifacts in Model-Driven Software Engineering. The monitoring of a transformation’s execution is a prerequisite to enable a software engineer to identify performance bottlenecks and improve transformations. Monitoring is particularly relevant for declarative model transformations since the order of execution is not explicitly defined but instead the result of internal heuristics of the transformation engine. In this paper, we present how we monitor the execution of Henshin model transformations using Kieker as well as the resulting monitoring overhead. We show that the monitoring overhead depends on the size of the input model and that it is between 17.03% and 28.44%.
  • Konferenzbeitrag
    Teaching the Use and Engineering of DSLs with JupyterLab: Experiences and Lessons Learned
    (Modellierung 2022, 2022) Charles, Joel; Jansen, Nico; Michael, Judith; Rumpe, Bernhard
    Domain-Specific Languages (DSLs) are tailored to a specific domain which requires them to provide domain-specific concepts and a sophisticated tooling for their engineering; aspects which we address with the language workbench MontiCore. As we use MontiCore for research and teaching, we are interested in reducing the entry barrier to use and engineer MontiCore DSLs. While there are approaches for ready-to-use learning environments such as web-based editors, only a few provide a tailored solution for specific DSLs. Within this paper, we present our experiences using JupyterLab in combination with the infrastructure of MontiCore for teaching the use and engineering of DSLs in an interactive manner. We have realized three practical courses and one conference tutorial applying this technical approach. The front-end provides immediate feedback and includes supporting explanations in an integrated manner. Initial feedback indicates that this approach can lower the entry barrier for DSL use and engineering for students and practitioners.
  • Konferenzbeitrag
    A Vision Towards Generated Assistive Systems for Supporting Human Interactions in Production
    (Modellierung 2022 Satellite Events, 2022) Michael, Judith
    Human workers need to cope with complex production settings when handling and monitoring cyber-physical production systems. Assistive systems can provide situational step-by-step support for human behavior, e.g., when interacting with a machine or for manual assembly. These systems need to take personal knowledge, workers skills or personal restrictions into account and are therefore subject to privacy concerns. However, the engineering of such interactive assistive systems within the production domain is a complex task as they might support critical functionality in dangerous environments and have a high need for safety and privacy considerations due to processing personal data. We want to investigate how the software engineering process of assistive systems in production can be improved to achieve higher reusability. Current research focuses on specific use cases and implements systems specifically for those needs without reusability in mind. We suggest using behavior and context models in a generative approach, to create a reusable method to engineer assistive systems for production environments, either as own applications or as services integrated within digital twins. We have already applied model-driven methods for assistive systems in the smart home domain and discuss the opportunities and challenges of an application of these methods for the production domain. These methods can facilitate the engineering of assistive functionalities within applications in production while meeting privacy, adaptability, and context-sensitivity requirements.