CAREER: GoldMine:Automatic Assertion Generation in System Design Using Data Mining and Static Analysis

职业：金矿：使用数据挖掘和静态分析的系统设计中的自动断言生成shobha vasudevan伊利诺伊大学厄巴纳香槟分校断言是设计意图的总结陈述，已成为硬件和软件开发周期中最流行的检查工件。在硬件方面，断言用于形式验证、动态验证、运行时监控、仿真、硅后调试和现场诊断。如今，断言的生成是一项非常手工的任务。在每个系统开发周期中，在断言生成中花费了许多人力和资源。本文针对数据挖掘和静态分析相结合的系统，提出了一种自动断言生成技术和工具GoldMine。数据挖掘通过对系统动态行为的统计分析来推断知识。静态分析在不执行的情况下对系统的源代码或模型进行分析，并分析可能的行为。这两种技术可以协同使用。静态分析技术可以用来推断领域信息，指导数据挖掘。GoldMine将提供目标设计的模拟数据以及对一套数据挖掘算法的静态分析，这些算法将推断出？可能不变量?。可能的不变量将通过正式的验证器来过滤真实的断言。来自形式验证的反馈将提供给挖掘算法，设计师/用户对生成的断言的评估也是如此。这个迭代过程将自动生成高质量的断言。在1000+处理器RTL上获得了初步结果。这里提出的将统计分析和静态分析结合起来以生成关于系统的知识是一种？元技术呢?这可以以多种形式使用。在任何结构化系统中提取和提供领域知识的系统方法，以及基于统计的学习技术，可以创建一个非常强大的组合。所介绍的静态领域分析与统计数据挖掘的结合，可用于软件系统、嵌入式系统等需要知识推断的领域。这个建议寻求自动化一个手动的、普适的系统过程。这旨在节省经济和人力资源，从而提高社会的生产力。GoldMine还将广泛传播（连同源代码）用于研究和教育目的，有助于学生的实际学习。系统设计界的优点是我们提出了对设计的人类认知方面的研究和系统化。

CAREER: GoldMine:Automatic Assertion Generation in System Design Using Data Mining and Static AnalysisShobha VasudevanUniversity of Illinois at Urbana ChampaignAssertions are summarized statements of design intent that have emerged as the most popular checking artifacts in hardware and software development cycles. In hardware, assertions are used in formal verification, dynamic verification, runtime monitoring, emulation, post-Silicon debug and in-field diagnosis. The generation of assertions today is an intensely manual task. During each system development cycle, many man-months and resources are spent in assertion generation. This work proposes an automatic assertion generation technique and tool, GoldMine, for systems using data mining and static analysis in combination. Data mining infers knowledge by statistical analysis of dynamic behavior of a system. Static analysis reasons with the source code or model of the system without executing it, and analyzes possible behavior. These two technologies can be used synergisti-cally. Static analysis techniques can be used to infer domain information and guide the data mining. GoldMine will provide simulation data of the target design along with static analysis to a suite of data mining algorithms that will infer ?likely invariants?. The likely invariants will be passed through a formal verifier for filtering the true assertions. Feedback from formal verification will be given to the mining algorithms, as will the designer/user evaluation of the generated assertions. This iterative process will produce high quality assertions automatically. Preliminary results have been obtained on Rigel, a 1000+ processor RTL. The marriage between statistical and static analyses proposed here for generating knowledge about a system is a ?meta technique? that can be used in a multitude of forms. A systematic methodology to extract and provide domain knowledge in any structured system, along with statistics based learning techniques can create a very powerful combination. The alliance of static domain analysis and statistical data mining that has been introduced can be used for software systems, embedded systems and other domains where knowledge needs to be inferred.This proposal seeks to automate a manual, all-pervasive system process. This aims at saving economic and human resources, thereby increasing the productivity of the community. GoldMine will also be widely disseminated (along with the source code) for research and education purposes, contributing to practical learning of students. The merit in the system design world is that we are proposing to study and systematize the human cognition aspect of design.