Automatic generation of compact formal properties for effective error detection

Michele Bertasi, G. D. Guglielmo, G. Pravadelli
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引用次数: 18

Abstract

Several approaches exist in literature for automatic extraction of model behaviours represented in the form of formal properties. Some of them rely on static analysis of the source code, others dynamically mine specifications by analysing simulation traces. In both cases, most of them work at bit level and generate properties in the form of combinational or temporal relationships among Boolean expressions. Such techniques are suited only for gate-level or RTL HW models. There are also approaches working on system-level descriptions and SW programs, but they generate properties to express only the sequential ordering of communication function calls and events, while the functional part of the implementation is ignored. To fill in the gap, this paper presents a dynamic methodology that works on gate-level, RTL and system-level HW descriptions as well as embedded SW, independently from the design model and the abstraction level. The generated properties are in the form of temporal relationships among arithmetic and logic expressions involving traditional HW description language data types (i.e., bit and logic vectors) as well as data types typically adopted in system-level models and SW programs (i.e., integer, double and string). A ranking function is also defined to classify the mined properties according to their capability of capturing meaningful design behaviours. Experimental results have shown that the approach allows generating compact properties really useful to effectively detect errors in the design implementation.
自动生成紧凑的形式属性,用于有效的错误检测
文献中存在几种自动提取以形式属性形式表示的模型行为的方法。其中一些依赖于源代码的静态分析,另一些则通过分析仿真跟踪动态挖掘规范。在这两种情况下,它们中的大多数都在位级别上工作,并以布尔表达式之间的组合或时间关系的形式生成属性。这种技术只适用于门级或RTL HW模型。也有处理系统级描述和软件程序的方法,但是它们生成的属性只表示通信函数调用和事件的顺序顺序,而忽略了实现的功能部分。为了填补这一空白,本文提出了一种动态方法,该方法可以独立于设计模型和抽象层,在门级、RTL和系统级硬件描述以及嵌入式软件上工作。生成的属性以算术和逻辑表达式之间的时间关系形式存在,这些表达式涉及传统的硬件描述语言数据类型(即位和逻辑向量)以及系统级模型和软件程序中通常采用的数据类型(即整数、双精度和字符串)。还定义了一个排序函数,根据挖掘属性捕获有意义的设计行为的能力对其进行分类。实验结果表明,该方法可以生成紧凑的属性,对于有效检测设计实现中的错误非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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