clusterfuzz
rules_js
clusterfuzz | rules_js | |
---|---|---|
3 | 2 | |
5,203 | 280 | |
0.5% | 0.7% | |
9.8 | 9.5 | |
2 days ago | 3 days ago | |
Python | Starlark | |
Apache License 2.0 | Apache License 2.0 |
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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
rules_js
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Any nice patterns for releasing libraries?
For Bazel you can find a good example of this functionality in rules_js with the integration of pnpm workspaces and the npm_package rule (macro) which recently gained a .publish runnable target that will publish the package.
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An ex-Googler's guide to dev tools
I also think it is partly as the tooling is not there yet - especially in the typical case when a project depends on lots of external dependencies.
I am looking forward to new set of bazel rules being worked on for eg. https://github.com/aspect-build/rules_js and https://github.com/jvolkman/rules_pycross which will makes it more idiomatic to work with existing language ecosystems.
What are some alternatives?
rules_pycross - Bazel + Python rules for cross-platform external dependencies
anchore-engine - A service that analyzes docker images and scans for vulnerabilities
mutant - Automated code reviews via mutation testing - semantic code coverage.
oss-fuzz - OSS-Fuzz - continuous fuzzing for open source software.
homebrew-tap - A private homebrew tap to install the Graphite CLI tools.
peafl64 - Static Binary Instrumentation tool for Windows x64 executables
rules_docker - Rules for building and handling Docker images with Bazel
pyfuzzer - Fuzz test Python modules with libFuzzer
scalar - Scalar: A set of tools and extensions for Git to allow very large monorepos to run on Git without a virtualization layer
redirector-rs - A dead simple human-writable URL redirector based loosely on google's `go/` system.