Grammarinator: Grammar-based Random Test Generator
ANTLRv4 grammar-based test generator
Grammarinator is a random test generator / fuzzer that creates test cases according to an input ANTLR v4 grammar. The motivation behind this grammar-based approach is to leverage the large variety of publicly available ANTLR v4 grammars.
The quick way:
pip3 install grammarinator
Or clone the project and run setuptools:
python3 setup.py install
As a first step, grammarinator takes an ANTLR v4 grammar and creates a test generator script in Python3. Such a generator can be subclassed later to customize it further if needed.
Example usage to create a test generator:
grammarinator-process <grammar-file(s)> -o <output-directory> --no-actions
Grammarinator uses the ANTLR v4 grammar format as its input, which makes existing grammars (lexer and parser rules) easily reusable. However, because of the inherently different goals of a fuzzer and a parser, inlined code (actions and conditions, header and member blocks) are most probably not reusable, or even preventing proper execution. For first experiments with existing grammar files, grammarinator-process supports the command-line option --no-actions, which skips all such code blocks during fuzzer generation. Once inlined code is tuned for fuzzing, that option may be omitted.
After having generated and optionally customized a fuzzer, it can be executed either by the grammarinator-generate script or by instantiating it manually.
Example usage of grammarinator-generate:
grammarinator-generate -l <unlexer> -p <unparser> -r <start-rule> -d <max-depth> \ -o <output-pattern> -n <number-of-tests> \ -t <one-or-more-transformer>
Real-life grammars often use recursive rules to express certain patterns. However, when using such rule(s) for generation, we can easily end up in an unexpectedly deep call stack. With the --max-depth or -d options, this depth - and also the size of the generated test cases - can be controlled.
Another speciality of the ANTLR grammars is that they support the so-called hidden tokens. These rules typically describe such elements of the target language that can be placed basically anywhere without breaking the syntax. The most common examples are comments or whitespaces. However, when using these grammars - which don’t define explicitly where whitespace may or may not appear in rules - to generate test cases, we have to insert the missing spaces manually. This can be done by applying various transformers (with the -t option) to the tree representation of the output tests. A simple transformer - that inserts a space after every unparser rule - is provided by grammarinator (grammarinator.runtime.simple_space_transformer).
As a final thought, one must not forget that the original purpose of grammars is the syntax-wise validation of various inputs. As a consequence, these grammars encode syntactic expectations only, and not semantic rules. If we still want to add semantic knowledge into the generated test, then we can inherit custom fuzzers from the generated ones and redefine methods corresponding to lexer or parser rules in ways that encode the required knowledge (e.g.: HTMLCustomUnparser).
The repository contains a minimal example to generate HTML files. To give it a try, run the processor first:
grammarinator-process examples/grammars/HTMLLexer.g4 \ examples/grammars/HTMLParser.g4 -o examples/fuzzer/
Then, use the generator to produce test cases:
grammarinator-generate -l examples/fuzzer/HTMLCustomUnlexer.py \ -p examples/fuzzer/HTMLCustomUnparser.py -r htmlDocument \ -o examples/tests/test_%d.html -t HTMLUnparser.html_space_transformer -n 100 -d 20
grammarinator was tested on:
Linux (Ubuntu 16.04 / 18.04)
Mac OS X (Sierra 10.12 / High Sierra 10.13 / Mojave 10.14)
Background on grammarinator is published in (R. Hodovan, A. Kiss, T. Gyimothy: “Grammarinator: A Grammar-Based Open Source Fuzzer”, A-TEST 2018).
Copyright and Licensing
Licensed under the BSD 3-Clause License.
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