摘要
为在语言解释器的模糊测试中构造符合语言规范的样本,并尽可能地得出异常测试结果以便发现漏洞,采用改进的概率上下文无关语法模型控制样本的变异过程,对变异结果中的未定义变量进行修正以提高符合语言规范的样本比率。在此基础上,对语言解释器进行模糊测试,结果表明,该测试所生成样本中符合语法、语义规范的比率高达96 %。
In order to construct samples that conform to language norms in fuzzy testing of language interpreter,and get abnormal test results as far as possible to find vulnerabilities,the improved Probabilistic Context Free Grammar(PCFG) model is used to control the variation process of samples,and the undefined variables in the variation results are modified to increase the ratio of samples that conform to language norms.On this basis,the language interpreter is tested by fuzzy testing.Results show that the ratio of samples generated by the test that conform to the grammatical and semantic norms is as high as 96 %.
作者
刘志昊
孙晓山
张阳
LIU Zhihao;SUN Xiaoshan;ZHANG Yang(Institute of Software,Chinese Academy of Sciences,Beijing 100190,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第8期22-24,30,共4页
Computer Engineering
基金
国家自然科学基金(61471344)
关键词
模糊测试
马尔科夫模型
概率上下文无关语法
机器学习
语言解释器
fuzzing testing
Markov model
Probabilistic Context Free Grammar(PCFG)
machine learning
language interpreter
