An exploratory study on performance engineering in model transformations

An exploratory study on performance engineering in model transformations
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模型转换中性能工程的探索性研究

DOI:
10.1145/3365438.3410950
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发表时间:
2020
期刊:
Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems
影响因子:
--
通讯作者:
Steffen Becker
Steffen Becker
中科院分区:
--
文献类型:
--
作者:
Raffaela Groner;Luis Beaucamp;Matthias Tichy;Steffen Becker

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模型驱动软件工程(Model-Driven Software Engineering,MDSE)是一种广泛使用的处理日益复杂的软件的方法。这种日益增长的复杂性还导致所使用的模型和应用的模型转换变得更大、更复杂。这意味着模型转换的执行性能变得越来越重要。虽然提高模型转换执行引擎的性能在过去一直是MDSE社区关注的焦点,但目前还没有任何关于模型转换开发人员如何处理性能问题的实证研究。因此,我们进行了探索性的混合方法研究,包括定量的在线调查和定性的访谈研究。我们使用问卷来调查转换的性能对于转换开发人员是否真的很重要,以及他们是否已经尝试提高模型转换的性能。随后,我们根据问卷的答案进行了半结构化访谈,调查转型开发人员如何处理性能问题,他们找到了什么原因和解决方案,以及他们认为什么可以帮助他们更容易地找到原因。量化在线调查的结果显示,81名参与者中有43名已经试图提高转换的性能,81名参与者中有34名有时或很少对执行性能感到满意。根据我们13次访谈的回答,我们确定了在模型转换中预防或发现性能问题的不同策略,以及性能问题的不同类型的原因和解决方案。最后,我们收集了受访者认为有助于解决性能问题的其他工具功能。
Model-Driven Software Engineering (MDSE) is a widely used approach to deal with the increasing complexity of software. This increasing complexity also leads to the fact that the models used and the model transformations applied become larger and more complex as well. This means that the execution performance of model transformations is gaining in importance. While improving the performance of model transformation execution engines has been a focus of the MDSE-community in the past, there does not exist any empirical study on how developers of model transformation deal with performance issues. Consequently, we conducted an exploratory mixed method study consisting of a quantitative online survey and a qualitative interview study. We used a questionnaire to investigate whether the performance of a transformation is actually important for transformation developers and whether they have already tried to improve the performance of a model transformation. Subsequently, we conducted semi-structured interviews based on the answers to the questionnaire to investigate how transformation developers deal with performance issues, what causes and solutions they found and also what they think could help them to easier find causes. The results of the quantitative online survey show that 43 of 81 participants have already tried to improve the performance of a transformation and 34 of the 81 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. Finally, we compiled a collection of additional tool features perceived helpful by the interviewees to address performance issues.
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