arXiv 1909.045032019-09cs.SEcs.LGstat.ML

ArduCode：自动化工程预测框架

ArduCode: Predictive Framework for Automation Engineering

Siemens Corporate Technology（西门子企业技术）；USC Information Sciences Institute（南加州大学信息科学研究所）

Arquimedes Canedo, Palash Goyal, Di Huang, Amit Pandey, Gustavo Quiros

摘要：

自动化工程是通过软件集成各种传感器，执行器和控制以实现现实世界过程自动化的任务。今天，自动化工程由一套软件工具支持，包括集成开发环境（IDE），硬件配置器，编译器和运行时。这些工具专注于自动化代码本身，但让自动化工程师在他们的决策过程中无人协助。这可能导致软件开发时间增加，因为决策的不完善导致软件和硬件之间的多次迭代。为了解决这个问题，本文定义了自动化工程中经常面临的多种挑战，并提出了使用机器学习的解决方案，以帮助工程师应对这些挑战。我们表明，可以利用机器学习来帮助自动化工程师对自动化进行分类，查找类似的代码片段，并对传感器和执行器的硬件选择进行推理。我们在两个真实的数据集上验证了我们的架构，其中包括2，927个Arduino项目和683个可编程逻辑控制器（PLC）项目。我们的研究结果表明，段落嵌入技术可以用于使用代码片段进行自动化分类，其精度接近人类注释，F1得分为72%。此外，我们表明，这样的嵌入技术可以帮助我们找到类似的代码片段，具有高精度。最后，我们使用自动编码器模型进行硬件推荐，并实现了0.79的p@3和0.95的p@5。

英文摘要：

Automation engineering is the task of integrating, via software, various sensors, actuators, and controls for automating a real-world process. Today, automation engineering is supported by a suite of software tools including integrated development environments (IDE), hardware configurators, compilers, and runtimes. These tools focus on the automation code itself, but leave the automation engineer unassisted in their decision making. This can lead to increased time for software development because of imperfections in decision making leading to multiple iterations between software and hardware. To address this, this paper defines multiple challenges often faced in automation engineering and propose solutions using machine learning to assist engineers tackle such challenges. We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators. We validate our architecture on two real datasets consisting of 2,927 Arduino projects, and 683 Programmable Logic Controller (PLC) projects. Our results show that paragraph embedding techniques can be utilized to classify automation using code snippets with precision close to human annotation, giving an F1-score of 72%. Further, we show that such embedding techniques can help us find similar code snippets with high accuracy. Finally, we use autoencoder models for hardware recommendation and achieve a p@3 of 0.79 and p@5 of 0.95.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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