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Auflistung nach Schlagwort "Model Transformation"

1 - 7 von 7
  • ConferencePaper
    Claimed Advantages and Disadvantages of (dedicated) Model Transformation Languages: A Systematic Literature Review
    (Software Engineering 2021, 2021) Götz, Stefan; Tichy, Matthias; Groner, Raffaela
    There exists a plethora of claims about the advantages and disadvantages of model transformation languages compared to general purpose programming languages. With our work, published at the Software and Systems Modelling Journal in 2020[GTG2020], we aim to create an overview over these claims in literature and systematize evidence thereof. For this purpose we conducted a systematic literature review by following a systematic process for searching and selecting relevant publications and extracting data. We selected a total of 58 publications, categorized claims about model transformation languages into 14 separate groups and conceived a representation to track claims and evidence through literature. From our results we conclude that: (i) current literature claims many advantages of model transformation languages but also points towards certain deficits and (ii) there is insufficient evidence for claimed advantages and disadvantages and (iii) a lack of research interest into the verification of claims.
  • Zeitschriftenartikel
    CVSM 2013 Challenge: Recognizing High-level Edit Operations in Evolving Models
    (Softwaretechnik-Trends: Vol. 33, No. 2, 2013) Kehrer, Timo; Gerth, Christian
    Timo Kehrer Software Engineering Group University of Siegen kehrer@informatik.uni-siegen.de Abstract
  • ConferencePaper
    An Exploratory Study on Performance Engineering in Model Transformations
    (Software Engineering 2021, 2021) Groner, Raffaela; Beaucamp, Luis; Tichy, Matthias; Becker, Steffen
    Model-Driven Software Engineering is used to deal with the increasing complexity of software, but this trend also leads to larger and more complex models and model transformations. While improving the performance of transformation engines has been a focus, there does not exist any empirical study on how transformation developers deal with performance issues. We used a quantitative questionnaire to investigate whether the performance of transformations is actually important for transformation developers. Based on the answers to the questionnaire, we conducted qualitative semi-structured interviews. The results of the online survey show that 43 of 81 participants have already tried to improve the performance of a transformation and 34 participants are sometimes or only rarely satisfied with the execution performance. Based on the answers from our 13 interviews, we identified different strategies to prevent or find performance issues in model transformations as well as different types of causes of performance issues and solutions to resolve them. We compiled a collection of tool features perceived helpful by the interviewees for finding causes. Overall, our results show that performance of transformations is relevant and that there is a lack of support for transformation developers without detailed knowledge of the engine to solve performance issues. This summary refers to our work, which was accepted for the Foundation Track of the ACM / IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS) in 2020.
  • Zeitschriftenartikel
    Integrating the Specification and Recognition of Changes in Models
    (Softwaretechnik-Trends: Vol. 32, No. 2, 2012) Kehrer, Timo; Kelter, Udo; Taentzer, Gabriele
    Timo Kehrer, Udo Kelter Praktische Informatik Universit¨ at Siegen {kehrer,kelter}@informatik.uni-siegen.de Abstract
  • Konferenzbeitrag
    Model-Driven Engineering for Machine Learning Code Generation using SysML
    (Modellierung 2024, 2024) Rädler, Simon; Rupp, Matthias; Rigger, Eugen; Rinderle-Ma, Stefanie
    The complexity of engineering products increases due to more functions, components, and the number of involved disciplines. In this respect, Data-Driven Engineering (DDE) aims to integrate machine learning to support product development and help manage the increasing complexity of engineered systems. Still, the potential and opportunities of DDE are not entirely reflected in practice, which among others originate from the rarely available machine learning experts on the market and the effort for the implementation in practice. In this respect, this work depicts an approach based on model-driven engineering, allowing to automatically derive executable machine learning code based on machine learning task formalization using the general-purpose modeling language SysML. The main focus of the approach is on the generality of the model transformation using templates so that extensions and changes to the code generation can be integrated without requiring profound modifications to the code generator. The approach is evaluated in a use case in the domain of Cyber-Physical Systems, i.e., weather forecast prediction based on data from a Cyber-Physical weather system. The derived executable code promises to reduce the time for the implementation and supports the standardization of machine learning implementations within a company due to templates.
  • Konferenzbeitrag
    On Controlling the Attack Surface of Object-Oriented Refactorings
    (Software Engineering 2020, 2020) Ruland, Sebastian; Kulcsár, Géza; Leblebici, Erhan; Peldszus, Sven; Lochau, Malte
    The results of this work have originally been published in. Refactorings constitute an effective means to improve quality and maintainability of evolving object-oriented programs. Search-based techniques have shown promising results in finding near-optimal sequences of behavior-preserving program transformations that (1) maximize code-quality metrics and (2) minimize the number of code changes. However, the impact of refactorings on non-functional properties like security has received little attention so far. To this end, we propose, as a further objective, to minimize the attack surface of object-oriented programs (i.e., to maximize strictness of declared accessibility of class members). Minimizing the attack surface naturally competes with applicability of established refactorings like MoveMethod, frequently used for improving code quality in terms of coupling/cohesion measures. Our tool implementation is based on an EMF meta-model for Java-like programs and utilizes MOMoT, a search-based model-transformation and optimization framework. Our experimental results gained from applying different accessibility-control strategies to a collection of real-world Java programs show the impact of attack surface minimization on design-improving refactorings. We further compare the results to those of existing refactoring tools.
  • Konferenz-Abstract
    Using Knowledge Graphs to Detect Enterprise Architecture Smells
    (EMISA 2022, 2022) Hacks, Simon; Smajevic, Muhamed; Bork, Dominik