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基于知识的资源受限嵌入式设备中受自然语言启发的连接编程模式验证。

Knowledge-Based Verification of Concatenative Programming Patterns Inspired by Natural Language for Resource-Constrained Embedded Devices.

机构信息

Department of Engineering, University of Palermo, Viale delle Scienze, Ed.6, 90128 Palermo, Italy.

Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), Via Ugo La Malfa, 153, 90146 Palermo, Italy.

出版信息

Sensors (Basel). 2020 Dec 26;21(1):107. doi: 10.3390/s21010107.

Abstract

We propose a methodology to verify applications developed following programming patterns inspired by natural language that interact with physical environments and run on resource-constrained interconnected devices. Natural language patterns allow for the reduction of intermediate abstraction layers to map physical domain concepts into executable code avoiding the recourse to ontologies, which would need to be shared, kept up to date, and synchronized across a set of devices. Moreover, the computational paradigm we use for effective distributed execution of symbolic code on resource-constrained devices encourages the adoption of such patterns. The methodology is supported by a rule-based system that permits runtime verification of Software Under Test (SUT) on board the target devices through automated oracle and test case generation. Moreover, verification extends from syntactic and semantic checks to the evaluation of the effects of SUT execution on target hardware. Additionally, by exploiting rules tying sensors and actuators to physical quantities, the effects of code execution on the physical environment can be verified. The system is also able to build test code to highlight software issues that may arise during repeated SUT execution on the target hardware.

摘要

我们提出了一种方法来验证基于受自然语言启发的编程模式开发的应用程序,这些应用程序与物理环境交互,并在资源受限的互联设备上运行。自然语言模式允许减少中间抽象层,将物理域概念映射到可执行代码中,避免使用需要共享、保持最新和在一组设备之间同步的本体。此外,我们用于在资源受限设备上有效执行符号代码的计算范例鼓励采用这种模式。该方法由基于规则的系统提供支持,该系统允许通过自动化预言机和测试用例生成在目标设备上对软件进行运行时验证。此外,验证不仅包括语法和语义检查,还包括对 SUT 执行对目标硬件的影响的评估。此外,通过利用将传感器和执行器与物理量联系起来的规则,可以验证代码执行对物理环境的影响。该系统还能够生成测试代码,以突出显示在目标硬件上重复执行 SUT 期间可能出现的软件问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5b2e/7795688/905fad63f0e3/sensors-21-00107-g001.jpg

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