clusterfuzz
peafl64
| clusterfuzz | peafl64 | |
|---|---|---|
| 3 | 1 | |
| 5,203 | 172 | |
| 0.5% | 0.6% | |
| 9.8 | 0.0 | |
| 2 days ago | 15 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU Affero General Public License v3.0 |
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clusterfuzz
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Fuzzing Ladybird with tools from Google Project Zero
https://github.com/google/clusterfuzz
At least Chromium has integrated multiple different fuzzers into their regular development workflow and found lots of bugs even before going public.
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An ex-Googler's guide to dev tools
Then it is clear that the behavior of this for loop is either not important or not being tested. This could mean that the tests that you do have are not useful and can be deleted.
> For most non-trivial software the possible state-space is enormous and we generally don't/can't test all of it. So "not testing the (full) behaviour of your application is the default for any test strategy", if we could we wouldn't have bugs... Last I checked most software (including Google's) has plenty of bugs.
I have also used (setup, fixed findings) using https://google.github.io/clusterfuzz/ which uses coverage + properties to find bugs in the way C++ code handles pointers and other things.
> The next question would be let's say I spend my time writing the tests to resolve this (could be a lot of work) is that time better spent vs. other things I could be doing? (i.e. what's the ROI)
That is something that will depend largely on the team and the code you are on. If you are in experimental code that isn't in production, is there value to this? Likely not. If you are writing code that if it fails to parse some data correctly you'll have a huge headache trying to fix it? Likely yes.
The SRE workbook goes over making these calculations.
> Even ignoring that is there data to support that the quality of software where mutation testing was added improved measurably (e.g. less bugs files against the deployed product, better uptime, etc?)
I know that there are studies that show that tests reduce bugs but I do not know of studies that say that higher test coverage reduces bugs.
The goal of mutation testing isn't to drive up coverage though. It is to find out what cases are not being exercised and evaluating if they will cause a problem. For example mutation testing tools have picked up cases like this:
if (debug) print("Got here!"); - ClusterFuzz is a scalable fuzzing infrastructure
peafl64
What are some alternatives?
rules_js - High-performance Bazel rules for running Node.js tools and building JavaScript projects
boofuzz - A fork and successor of the Sulley Fuzzing Framework
rules_pycross - Bazel + Python rules for cross-platform external dependencies
Raccoon - A high performance offensive security tool for reconnaissance and vulnerability scanning
anchore-engine - A service that analyzes docker images and scans for vulnerabilities
Obfu-DE-Scate - Obfu[DE]scate is a de-obfuscation tool for Android APKs that uses fuzzy comparison logic to identify similarities between functions, even if they have been renamed as part of obfuscation. It compares two versions of an APK and generates a mapping text file and an interactive HTML file as outputs!
oss-fuzz - OSS-Fuzz - continuous fuzzing for open source software.
hypothesis - Hypothesis is a powerful, flexible, and easy to use library for property-based testing.
pyfuzzer - Fuzz test Python modules with libFuzzer
dirsearch - Web path scanner
mutant - Automated code reviews via mutation testing - semantic code coverage.
dnstwist - Domain name permutation engine for detecting homograph phishing attacks, typo squatting, and brand impersonation