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Auflistung nach Autor:in "Groner, Raffaela"

1 - 5 von 5
  • 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.
  • 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.
  • 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
    Predicting the Performance of ATL Model Transformations
    (Software Engineering 2024 (SE 2024), 2024) Groner, Raffaela; Bellmann, Peter; Höppner, Stefan; Thiam, Patrick; Schwenker, Friedhelm; Tichy, Matthias
  • Konferenzbeitrag
    A Survey on the Relevance of the Performance of Model Transformations
    (Software Engineering 2022, 2022) Groner, Raffaela; Juhnke, Katharina; Höppner, Stefan; Tichy, Matthias; Becker, Steffen; Vijayshree, Vijayshree; Frank, Sebastian
    When we are confronted with performance issues in a general-purpose language, like Java, it is a given to us that we have various tools and techniques at our disposal to help us. But is such support also needed when using model transformation languages? To address this question, we conducted a quantitative online survey as part of a mixed methods study with 84 respondents to our questionnaire. Our results show that a certain performance is desired but not always achieved. The developers would like to improve the performance, but they lack insights on how a transformation is performed. As a first step to mitigate this issue, we compiled a list of information regarding the models used, the transformations applied and their execution deemed to be helpful by the participants. Additionally, we used hypotheses tests to investigate possible influencing factors that cause participants to try to improve the performance of transformations. The main relevant factors found in our study are the satisfaction with the execution time, the size of the models used, the relevance of whether a certain execution time is not exceeded in the average case, and the knowledge of how a transformation engine executes a transformation.