Look Ma, No Input Samples! Mining Input Grammars from Code with Symbolic Parsing

Generating test inputs at the system level (“fuzzing”) is most effective if one has a complete specification (such as a grammar) of the input language. In the absence of a specification, all known fuzzing approaches rely on a set of input samples to infer input properties and guide test generation. If the set of inputs is incomplete, however, so will be the resulting test cases; if one has no input samples, meaningful test generation so far has been hard to impossible. In this paper, we introduce a means to determine the input language of a program from the program code alone, opening several new possibilities for comprehensive testing of a wide range of programs. Our symbolic parsing approach first transforms the program such that (1) ‍calls to parsing functions are abstracted into parsing corresponding symbolic nonterminals, and (2) ‍loops and recursions are limited such that the transformed parser then has a finite set of paths. Symbolic testing then associates each path with a sequence of symbolic nonterminals and terminals, which form a grammar. First grammars extracted from nontrivial C subjects by our prototype show very high recall and precision, enabling new levels of effectiveness, efficiency, and applicability in test generators.