How is this different from the Integer Digit Error Introducer?

The underlying mechanism is identical: both independently roll each digit against a rate and replace mutated ones with a different digit, leaving the sign untouched. This tool is framed for open-ended fuzz-testing (throwing randomized noise at a parser to see what breaks) rather than controlled, precise corruption of specific test cases, and it defaults to a different mutation rate and seed to match that framing.

What does the mutation rate control?

It's a percentage (0-100) representing the independent chance that any given digit gets replaced with a different digit. Higher rates produce noisier, more heavily fuzzed integers; a rate of 0% leaves the list untouched.

Why use a seeded PRNG for something meant to be randomized fuzzing?

Fuzzing benefits from randomness, but reproducibility matters just as much: if a fuzzed input reveals a bug, you need to be able to regenerate that exact same input later. A seeded mulberry32 PRNG gives both, pseudo-random-looking output that's exactly reproducible from the same seed.

Does fuzzing ever change how many digits an integer has?

No. Every digit position is mutated in place to a different digit; the tool never inserts or removes digits, so the fuzzed integer always has the same digit count as the original (the sign character, if any, is also preserved).

Integer Digit Fuzzer - Randomized Digit-Level Fuzz Input

Overview

Introduction

This tool generates randomized fuzz input from a list of integers by independently mutating each digit at a chosen rate, useful for stress-testing how a parser or numeric input handler copes with noisy data.

It uses a seeded pseudo-random generator so a fuzzing run that turns up a bug can be exactly reproduced later from the same input, rate, and seed.

What Is Integer Digit Fuzzer?

A digit-level fuzz-input generator for lists of integers, one value per line, where every digit character is an independent candidate for random mutation.

At the chosen mutation rate, a digit is replaced with a different, randomly chosen digit 0-9; the sign character, if present, always passes through untouched.

How Integer Digit Fuzzer Works

The list is parsed and validated first; every non-blank line must be a plain integer, or the tool reports which line failed.

A mulberry32 pseudo-random generator, seeded from your chosen seed value, rolls against the mutation rate for every digit of every integer in turn.

Digits selected for mutation are replaced with a guaranteed-different digit (via a random 1-9 offset, mod 10); the sign character is sliced off before this process and reattached afterward.

When To Use Integer Digit Fuzzer

Use it to generate a batch of randomized-but-reproducible fuzzed integers for stress-testing a parser, deserializer, or numeric input handler.

It's also useful for property-based-testing style workflows where you want many slightly-varied malformed inputs derived from a known-good seed list.

Often used alongside Integer Digit Error Introducer, Integer Fuzzer and Integer Error Introducer.

Features

Advantages

Reproducible randomness: the same input, rate, and seed always regenerate the exact same fuzzed output, so a bug-triggering case can be replayed.
The mutation rate gives continuous control over fuzzing intensity, from light noise to heavy corruption.
Preserves digit count and sign, so fuzzed values stay recognizably related to their originals for easier debugging.

Limitations

Fuzzed integers can end up with leading zeros, since digit mutation intentionally doesn't renormalize the result.
Only understands plain decimal integers, one per line; it has no notion of floating-point or other numeric formats.
The randomness is deterministic given a seed, which is a feature for reproducibility but means it isn't a source of true entropy.

Examples

50% mutation rate with a fixed seed

Input

123
-4560
789

Output

223
-4010
989

At a 50% rate and seed 7, roughly half the digits were independently mutated, and the sign on -4560 was preserved.

0% mutation rate leaves the list untouched

Input

123
-4560
789

Output

123
-4560
789

Best Practices & Notes

Best Practices

Record the seed used for any fuzzing run that surfaces a bug, so the exact failing input can be regenerated.
Sweep across a range of mutation rates to find the threshold where your parser starts failing.

Developer Notes

Mechanically identical to the Integer Digit Error Introducer's mulberry32-seeded per-digit mutation approach, kept as a separate lib/tool with its own default mutation rate (30%) and seed (42) and fuzz-testing-focused copy, since the two tools serve different framings (controlled corruption vs. open-ended fuzz input) despite sharing an implementation shape.

Integer Digit Fuzzer Use Cases

Stress-testing an integer parser or deserializer with reproducible randomized noise
Generating a batch of fuzzed test inputs derived from a known-good seed list
Comparing parser robustness across a sweep of mutation rates

Common Mistakes

Confusing this tool's fuzzing framing with the Integer Digit Error Introducer's controlled-corruption framing; they behave the same way but are meant for different workflows.
Forgetting to record the seed when a fuzzed input reveals a bug, making it hard to reproduce later.
Assuming a high mutation rate always produces a wildly different-looking number; short integers have fewer digits to mutate, so the effect is naturally smaller.

Tips

Start at the default 30% rate and adjust up or down based on how aggressively you want to fuzz.
Use several different seeds at the same rate to get a broader spread of fuzzed test cases.

Integer Digit Fuzzer