一句话：提出从上下文无关文法自动挖掘约束并生成属性文法，降低高效基于文法模糊测试的门槛。
基于文法的模糊测试能显著提升处理结构化输入程序的缺陷检测能力，但文法多依赖手工编写，需要专业知识，因而难以在大规模模糊器中独立使用。现有工作可从被测程序自动提取上下文无关文法，但所得文法表达力不足，会生成大量错误输入，效果难以与其他模糊测试技术竞争。本文提出一种自动化技术，从上下文无关文法构建属性文法，通过挖掘约束来增强文法表达力，从而显著降低高效、大规模基于文法模糊测试的进入门槛。摘要未报告具体实验，评估可能关注生成输入的合法性与缺陷发现能力。
Grammar-based fuzzing has been shown to significantly improve bug detection in programs with highly structured inputs. However, since grammars are largely handwritten, it is rarely used as a standalone technique in large-spectrum fuzzers as it requires human expertise. To fill this gap, promising techniques begin to emerge to automate the extraction of context-free grammars directly from the program under test. Unfortunately, the resulting grammars are usually not expressive enough and generate too many wrong inputs to provide results capable of competing with other fuzzing techniques. In this paper we propose a technique to automate the creation of attribute grammars from context-free grammars, thus significantly lowering the barrier of entry for efficient and effective large-scale grammar-based fuzzing.
DOI 原文 ·
@inproceedings{paperbot5388,
title = {Mining constraints for grammar fuzzing},
author = {Michaël Mera},
booktitle = {ISSTA},
year = {2019},
doi = {10.1145/3293882.3338983}
}