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Mining Input Grammars from Dynamic Taints

Event date: 
Wednesday, 20 September, 2017 - 17:00
Roberto Tiella

On "Mining Input Grammars from Dynamic Taints" M. Hoeschele and A. Zeller, ASE 2016.

Abstract (of the paper):

Knowing which part of a program processes which parts of an in-
put can reveal the structure of the input as well as the structure of
the program. In a URL,
for instance, the protocol http, the host,
and the path path would be handled by different functions and
stored in different variables. Given a set of sample inputs, we
use dynamic tainting to trace the data flow of each input character,
and aggregate those input fragments that would be handled by the
same function into lexical and syntactical entities. The result is a
context-free grammar that reflects valid input structure. In its eval-
uation, our AUTOGRAM prototype automatically produced readable
and structurally accurate grammars for inputs like URLs , spread-
sheets or configuration files. The resulting grammars not only allow
simple reverse engineering of input formats, but can also directly
serve as input for test generators.

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