tree: fae30ff42d9506f5d46f5553649cb15dbe6b53b6 [path history] [tgz]
  1. README.md
  2. dartfuzz.py
  3. run_dartfuzz_test.py
runtime/tools/dartfuzz/README.md

DartFuzz

DartFuzz is a tool for generating random programs with the objective of fuzz testing the Dart project. Each randomly generated program can be run under various modes of execution, such as using JIT, using AOT, using JavaScript after dart2js, and using various target architectures (x86, arm, etc.). Any difference between the outputs (divergence) may indicate a bug in one of the execution modes.

How to run DartFuzz

dartfuzz.py [--help] [--seed SEED]

where

--help : prints help and exits
--seed : defines random seed (system-set by default)

DartFuzz sends all output to stdout, and provides a runnable main isolate. A typical test run looks as:

dartfuzz.py > fuzz.dart
dart fuzz.dart

How to start DartFuzz testing

run_dartfuzz_test.py  [--help]
                      [--repeat REPEAT]
                      [--true_divergence]
                      [--mode1 MODE]
                      [--mode2 MODE]

where

--help            : prints help and exits
--repeat          : number of tests to run (1000 by default)
--true_divergence : only report true divergences
--mode1           : m1
--mode2           : m2, and values one of
    jit-ia32  = Dart JIT (ia32)
    jit-x64   = Dart JIT (x64)
    jit-arm32 = Dart JIT (simarm)
    jit-arm64 = Dart JIT (simarm64)
    aot-x64   = Dart AOT (x64)
    aot-arm64 = Dart AOT (simarm64)
    js        = dart2js + JS

This fuzzer tool assumes the environment variable ‘DART_TOP’ points to the top of the Dart SDK development tree in which all proper binaries have been built already (e.g. testing jit-ia32 will invoke the binary ${DART_TOP}/out/ReleaseIA32/dart to start the Dart VM).

Background

Although test suites are extremely useful to validate the correctness of a system and to ensure that no regressions occur, any test suite is necessarily finite in size and scope. Tests typically focus on validating particular features by means of code sequences most programmers would expect. Regression tests often use slightly less idiomatic code sequences, since they reflect problems that were not anticipated originally, but occurred “in the field”. Still, any test suite leaves the developer wondering whether undetected bugs and flaws still linger in the system.

Over the years, fuzz testing has gained popularity as a testing technique for discovering such lingering bugs, including bugs that can bring down a system in an unexpected way. Fuzzing refers to feeding a large amount of random data as input to a system in an attempt to find bugs or make it crash. Generation- based fuzz testing constructs random, but properly formatted input data. Mutation-based fuzz testing applies small random changes to existing inputs in order to detect shortcomings in a system. Profile-guided or coverage-guided fuzzing adds a direction to the way these random changes are applied. Multi- layered approaches generate random inputs that are subsequently mutated at various stages of execution.

The randomness of fuzz testing implies that the size and scope of testing is no longer bounded. Every new run can potentially discover bugs and crashes that were hereto undetected.