Improving Fitness Function for Language Fuzzing with PCFG Model

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@InProceedings{Sun:2018:COMPSAC,

• author = "Xiaoshan Sun and Yu Fu and Yun Dong and Zhihao Liu and Yang Zhang2",
• booktitle = "2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)",
• title = "Improving Fitness Function for Language Fuzzing with PCFG Model",
• year = "2018",
• volume = "01",
• pages = "655--660",
• abstract = "In this paper, we propose to use machine learning techniques to model the vagueness of bugs for language interpreters and develop a fitness function for the language fuzzing based on genetic programming. The basic idea is that bug-triggering scripts usually contain uncommon usages which are not likely used by programmers in daily developments. We capture the uncommonness by using the probabilistic context-free grammar model and the Markov model to compute the probabilities of scripts such that bug-triggering scripts will get lower probabilities and higher fitness values. We choose the ROC (Receiver Operating Characteristic) curves to evaluate the performance of fitness functions in identifying bug-triggering scripts from normal scripts. We use a large corpus of JavaScript scripts from Github and POC test cases of bug-reports from SpiderMonkey's bugzilla for evaluations. The ROC curves from the experiments show that our method can provide better ability to rank the bug triggering scripts in the top-K elements.",
• keywords = "genetic algorithms, genetic programming, SBSE, language fuzzing, probabilistic context-free grammar, evolutionary algorithm, script ranking, Markov chain",
• DOI = "doi:10.1109/COMPSAC.2018.00098",
• ISSN = "0730-3157",
• month = jul,
• notes = "Also known as \cite{8377723}",

}

Genetic Programming entries for Xiaoshan Sun Yu Fu Yun Dong Zhihao Liu Yang Zhang2

Citations