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P320 - Software Engineering 2022

https://dl.gi.de/handle/20.500.12116/37960
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Auflistung P320 - Software Engineering 2022 nach Schlagwort "ATL"

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  • Konferenzbeitrag
    Contrasting Dedicated Model Transformation Languages vs. General Purpose Languages: A Historical Perspective on ATL vs. Java based on Complexity and Size - Extended Abstract
    (Software Engineering 2022, 2022) Höppner, Stefan; Kehrer, Timo; Tichy, Matthias
    Model transformations are one key concept of model-driven engineering, and model transformation languages (MTLs) emerged with its popularity about 15 to 20 years ago. MTLs claim to ease model transformation development by abstracting from recurring transformation aspects and hiding complex semantics behind simple and intuitive syntax. Nonetheless, MTLs are rarely adopted in practice, there is still no empirical evidence for the claim of easier development, and the argument of abstraction deserves a fresh look in the light of modern general-purpose languages (GPLs) which have undergone a significant evolution in the last two decades. In our SoSyM paper, we report on a study in which we compare the complexity and size of model transformations written in three different languages, namely (i) the Atlas Transformation Language (ATL), (ii) Java SE5 (2004-2009), and (iii) Java SE14 (2020); the Java transformations are derived from an ATL specification using a translation schema we developed. Based on the results of these comparisons, we discuss the concrete advancements in newer Java versions. We also discuss to which extent new language advancements justify writing transformations in a GPL rather than a dedicated MTL. We further indicate potential avenues for future research on the comparison of MTLs and GPLs.
  • 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.