领域特定语言的通用静态分析框架

Avijit Mandal, Devina Mohan, R. Jetley, Sreeja Nair, Meenakshi D'Souza
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引用次数: 6

摘要

用于监视和控制自动化系统内操作的软件是使用领域特定语言定义的。控制代码中的潜在错误,如果未被发现,可能导致意想不到的系统故障,危及自动化系统的安全性和安全性。传统的分析技术不足以检测此类错误,因为它们不能专门满足底层特定于领域的语言。然而,考虑到不同自动化领域的多样性,没有分析这些语言的标准平台。本文针对自动化领域中使用的特定领域语言,提出了一个通用的静态分析框架。分析方法详尽地检测控制代码中的运行时错误,并确保遵循良好的编程实践。这些运行时错误和编码违例将根据抽象语法树和源自代码的控制流图进行检查。数据流分析(DFA)、抽象解释和基于模式的匹配技术用于识别控制语言的特定领域错误和编码违规。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Generic Static Analysis Framework for Domain-specific Languages
Software used to monitor and control operations within an automation system is defined using domain-specific languages. Latent errors in the control code, if left undetected, can lead to unexpected system failures compromising the safety and the security of the automation system. Traditional analysis techniques are insufficient to detect such errors as they do not cater specifically to the underlying domain-specific language. However, given the diversity of different automation domains, there is no standard platform for analysis of these languages. This paper proposes a generic static analysis framework for domain-specific languages used in the automation domain. The analysis approach exhaustively detects runtime errors in control code and ensures compliance to good programming practices. These runtime errors and coding violations are checked against abstract syntax trees and control flow graphs derived from the code. Data Flow Analysis (DFA), Abstract interpretation and pattern-based matching techniques are used to identify domain specific errors and coding violations for control languages.
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