A set of metrics for characterizing simulink model comprehension
A set of metrics for characterizing simulink model comprehension
复制标题
一组用于表征 simulink 模型理解的指标
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Fabbri
中科院分区：
文献类型：
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作者：
E. A. Antonio;F. Ferrari;G. Caurin;S. Fabbri
Simulink是一种强大的嵌入式系统建模工具，在动态系统建模中发挥着重要作用。然而，很少有人关注Simulink模型的质量。此外，还没有研究发现模型复杂性与其对Simulink模型理解质量的影响之间的关系。本文的目的是定义一组指标来支持Simulink模型的表征，并探讨它们与模型综合属性的关系。在这项研究中，我们进行了一个控制实验，使用两个版本的机器人Simulink模型-其中一个是通过特设的开发方法，另一个是通过重新设计的开发方法。实验结果表明，重组后的模型比adhoc模型更易于理解。总之，从每个版本的Simulink模型中收集的一组指标表明与模型理解存在反比关系，即，度量越低，模型的理解能力越强。
Simulink is a powerful tool for Embedded Systems, playing a key role in dynamic systems modeling. However, far too little attention has been paid to quality of Simulink models. In addition, no research has been found linking the relationship between model complexity and its impact in the comprehension quality of Simulink models. The aim of this paper is to define a set of metrics to support the characterization of Simulink models and to investigate their relationship with the model comprehension property. For this study, we performed a controlled experiment using two versions of a robotic Simulink model — one of them was constructed through the ad hoc development approach and the other one through the re-engineered development approach. The results of the experiment show that the re-engineered model is more comprehensible than the ad hoc model. In summary, the set of metrics collected from each version of the Simulink model suggests an inverse relationship with the model comprehension, i.e., the lower the metrics, the greater the model comprehension.