Data Set for Enhanced Performance Prediction of ATL Model Transformations

Model transformation languages are domain-specific languages, which are designed to comfortably define transformations. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. In particular, we focused on how to predict the performance of transformations that also transform attributes whose values can have arbitrary size. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained. Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models we used. In this data set, we provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models. A detailed documentation is available in documentaion.pdf.模型转换语言（Model transformation languages）是一类领域特定语言（domain-specific languages），专为便捷定义转换操作而设计。随着转换技术在各领域的应用愈发广泛，输入模型的复杂度与规模也同步提升。然而开发者往往缺乏适用于性能测试的适配模型。为此我们开展了相关实验，基于输入模型的特征，借助机器学习方法预测模型转换的性能表现，其中重点研究了如何预测可处理值为任意大小属性的转换操作的性能。本数据集包含我们的原始与处理后的输入数据、复现实验所需的脚本，以及实验所得结果。我们的输入数据包含针对Atlas转换语言（Atlas Transformation Language, ATL）中定义的六种不同转换操作的耗时测量结果，以及我们所使用的真实世界输入模型的采集特征。本数据集附带实现我们实验的脚本。我们采用线性回归（linear regression）、随机森林（random forests）以及基于径向基函数核（radial basis function kernel）的支持向量回归（support vector regression）三种机器学习方法，预测ATL转换操作的执行耗时。我们还针对不同的输入模型特征集合，测试其作为机器学习模型输入的效果，相关细节详见提供的documentation.pdf文件。实验结果以原始数据形式存储于各个CSV文件中。此外，我们还提供了用于采集指定模型集合特征的Eclipse插件（Eclipse plugin），详细文档可参阅documentaion.pdf文件。