HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
dvyukov
go-fuzz
Go2015–2022
dvyukov / go-fuzz

go-fuzz

Randomized testing for Go

20Distinct people
20Mentions
0Explicit recommenders
4.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Randomized testing for Go

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:49:33 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year20 dated mentions

Observed mentions: 2015-04-27 12:58:29 UTC to 2022-03-29 18:11:36 UTC.

02468102015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 9 mentions; 5 submissions, 4 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2022

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC954
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC000
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC321
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC101
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC303

From the project README

An excerpt from the repository, fetched Nov 13, 2026.

Read the project’s introduction
go-fuzz: randomized testing for Go Go-fuzz is a coverage-guided fuzzing solution for testing of Go packages. Fuzzing is mainly applicable to packages that parse complex inputs (both text and binary), and is especially useful for hardening of systems that parse inputs from potentially malicious users (e.g. anything accepted over a network). Note: go-fuzz has recently added preliminary support for fuzzing Go Modules. See the section below for more details. If you encounter a problem with modules, please file an issue with details. A workaround might be to disable modules via export GO111MODULE=off. Usage First, you need to write a test function of the form: func Fuzz(data []byte) int Data is a random input generated by go-fuzz, note that in most cases it is invalid. The function must return 1 if the fuzzer should increase priority of the given input during subsequent fuzzing (for example, the input is lexically correct and was parsed successfully); -1 if the input must not be added to corpus even if gives new coverage; and 0 otherwise; other values are reserved for future use. The Fuzz function must be in a package that go-fuzz can import. This means the code you want to test can't be in package main. Fuzzing internal packages is supported, however. In its basic form the Fuzz function just parses the input, and go-fuzz ensures that it does not panic, crash the program, allocate insane amount of memory nor hang. Fuzz function can also do application-level checks, which will make testing more efficient (discover more bugs). For example, Fuzz function can serialize all inputs that were successfully deserialized, thus ensuring that serialization can handle everything deserialization can produce. Or, Fuzz function can deserialize-serialize-deserialize-serialize and check that results of first and second serialization are equal. Or, Fuzz function can feed the input into two different implementations (e.g. dumb and optimized) and check that the output is equal. To communicate application-level bugs Fuzz function should panic (os.Exit(1) will work too, but panic message contains more info). Note that Fuzz function should not output to stdout/stderr, it will slow down fuzzing and nobody will see the output anyway. The exception is printing info about a bug just before panicking. Here is an example of a simple Fuzz function for image/png package: package png import ( "bytes" "image/png" ) func Fuzz(data []byte) int { png.Decode(bytes.NewReader(data)) return 0 } A more useful Fuzz function would look like: func Fuzz(data []byte) int { img, err := png.Decode(bytes.NewReader(data)) if err != nil { if img != nil { panic("img != nil on error") } return 0 } var w bytes.Buffer err = png.Encode(&w, img) if err != nil { panic(err) } return 1 } The second step is collection of initial input corpus. Ideally, files in the corpus are as small as possible and as diverse as possible. You can use inputs used by unit tests and/or generate them. For example, for an image decoding package you can encode several small bitmaps (black, random noise, white with few non-white pixels) with different levels of compressions and use that as the initial corpus. Go-fuzz will deduplicate and minimize the inputs. So throwing in a thousand of inputs is fine, diversity is more important. Put the initial corpus into the workdir/corpus directory (in our case examples/png/corpus). Go-fuzz will add own inputs to the corpus directory. Consider committing the generated inputs to your source control system, this will allow you to restart go-fuzz without losing previous work. The go-fuzz-corpus repository contains a bunch of examples of test functions and initial input corpuses for various packages. The next step is to get go-fuzz: $ go install github.com/dvyukov/go-fuzz/go-fuzz@latest github.com/dvyukov/go-fuzz/go-fuzz-build@latest Then, download the corpus and build the test program with necessary instrumentation: $ git clone https://github.com/dvyukov/go-fuzz-corpus.git $ cd go-fuzz-corpus $ cd png $ go-fuzz-build This will produce png-fuzz.zip archive. Now we are ready to go: $ go-fuzz Go-fuzz will generate and test various inputs in an infinite loop. Workdir is used to store persistent data like current corpus and crashers, it allows fuzzer to continue after restart. Discovered bad inputs are stored in workdir/crashers dir; where file without a suffix contains binary input, file with .quoted suffix contains quoted input that can be directly copied into a reproducer program or a test, file with .output suffix contains output of the test on this input. Every few seconds go-fuzz prints logs to stderr of the form: 2015/04/25 12:39:53 workers: 500, corpus: 186 (42s ago), crashers: 3, restarts: 1/8027, execs: 12009519 (121224/sec), cover: 2746, uptime: 1m39s Where workers means number of tests running in parallel (set with -procs flag). corpus is current number of interesting inputs the fuzzer has discovered, time in brackets says when the last interesting input was discovered. crashers is number of discovered bugs (check out workdir/crashers dir). restarts is the rate with which the fuzzer restarts test processes. The rate should be close to 1/10000 (which is the planned restart rate); if it is considerably higher than 1/10000, consider fixing already discovered bugs which lead to frequent restarts. execs is total number of test executions, and the number in brackets is the average speed of test executions. cover is number of bits set in a hashed coverage bitmap, if this number grows fuzzer uncovers new lines of code; size of the bitmap is 64K; ideally cover value should be less than ~5000, otherwise fuzzer can miss new interesting inputs due to hash collisions. And finally uptime is uptime of the process. This same information is also served via http (see the -http flag). Modules support go-fuzz has preliminary support for fuzzing Go Modules. go-fuzz respects the standard GO111MODULE environment variable
Read the full README on GitHub

What Hacker News says

0 sources

No sources match this period.

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.