/ 
App-Test-Generator-0.13
/ App::Test::Generator

NAME

App::Test::Generator - Generate fuzz and corpus-driven test harnesses

VERSION

Version 0.13

SYNOPSIS

From the command line:

fuzz-harness-generator t/conf/add.yml > t/add_fuzz.t

From Perl:

use App::Test::Generator qw(generate);

# Generate to STDOUT
App::Test::Generator::generate("t/conf/add.yml");

# Generate directly to a file
App::Test::Generator::generate('t/conf/add.yml', 't/add_fuzz.t');

OVERVIEW

This module takes a formal input/output specification for a routine or method and automatically generates test cases. In effect, it allows you to easily add comprehensive black-box tests in addition to the more common white-box tests that are typically written for CPAN modules and other subroutines.

The generated tests combine:

  • Random fuzzing based on input types

  • Deterministic edge cases for min/max constraints

  • Static corpus tests defined in Perl or YAML

This approach strengthens your test suite by probing both expected and unexpected inputs, helping you to catch boundary errors, invalid data handling, and regressions without manually writing every case.

DESCRIPTION

This module implements the logic behind fuzz-harness-generator. It parses configuration files (fuzz and/or corpus YAML), and produces a ready-to-run .t test script using Test::Most.

It reads configuration files in any format (including Perl .conf with our variables, though this format will be deprecated in a future release) and optional YAML corpus files, and generates a Test::Most-based fuzzing harness combining:

  • Randomized fuzzing of inputs (with edge cases)

  • Optional static corpus tests from Perl %cases or YAML file (yaml_cases key)

  • Functional or OO mode (via $new)

  • Reproducible runs via $seed and configurable iterations via $iterations

CONFIGURATION

The configuration file is either a file that can be read by Config::Abstraction or a trusted input Perl file that should set variables with our.

The documentation here covers the old trusted input style input, but that will go away so you are recommended to use Config::Abstraction files. Example: the generator expects your config to use our %input, our $function, etc.

SCHEMA

Recognized items:

  • %input - input params with keys => type/optional specs:

    When using named parameters

    input:
  name:
    type: string
    optional: false
  age:
    type: integer
    optional: true

    Supported basic types used by the fuzzer: string, integer, number, boolean, arrayref, hashref. (You can add more types; they will default to undef unless extended.)

    For routines with one unnamed parameter

    input:
  type: string

    Currently, routines with more than one unnamed parameter are not supported.

    The keyword undef is used to indicate that the function takes no arguments.

  • %output - output param types for Return::Set checking:

    output:
  type: string

    If the output hash contains the key _STATUS, and if that key is set to DIES, the routine should die with the given arguments; otherwise, it should live. If it's set to WARNS, the routine should warn with the given arguments. The output can be set to the string 'undef' if the routine should return the undefined value:

    ---
module: Scalar::Util
function: blessed

input:
  arg1: string

output: undef

    The keyword undef is used to indicate that the function returns nothing.

  • %transforms - list of transformations from input sets to output sets

    It takes a list of subsets of the input and output definitions, and verifies that data from each input subset is correctly transformed into data from the matching output subset.

    This is a draft definition of the schema.

    ---
module: builtin
function: abs

config:
  test_undef: no
  test_empty: no
  test_nuls: no

input:
  number:
    type: number
    position: 0
output:
  type: number
  min: 0
transforms:
  positive:
    input:
      number:
        type: number
        position: 0
        min: 0
    output:
      type: number
      min: 0
  negative:
    input:
      number:
        type: number
        position: 0
        max: 0
    output:
      type: number
      min: 0
  error:
    input:
      undef
    output:
      _STATUS: DIES

    If the output hash contains the key _STATUS, and if that key is set to DIES, the routine should die with the given arguments; otherwise, it should live. If it's set to WARNS, the routine should warn with the given arguments.

    The keyword undef is used to indicate that the function returns nothing.

  • $module - module name (optional).

    Using the reserved word builtin means you're testing a Perl builtin function.

    If omitted, the generator will guess from the config filename: My-Widget.conf -> My::Widget.

  • $function - function/method to test (defaults to run).

  • $new - optional hashref of args to pass to the module's constructor (object mode):

    new:
  api_key: ABC123
  verbose: true

    To ensure new() is called with no arguments, you still need to define new, thus:

    module: MyModule
function: my_function

new:

    For the legacy Perl variable syntax, use the empty string:

    our $new = '';

  • %cases - optional Perl static corpus, when the output is a simple string (expected => [ args... ]):

    Maps the expected output string to the input and _STATUS

    cases:
  ok:
    input: ping
    status: OK
  error:
    input: ""
    status: DIES

  • $yaml_cases - optional path to a YAML file with the same shape as %cases.

  • $seed - optional integer. When provided, the generated t/fuzz.t will call srand($seed) so fuzz runs are reproducible.

  • $iterations - optional integer controlling how many fuzz iterations to perform (default 50).

  • %edge_cases - optional hash mapping of extra values to inject:

    # Two named parameters
our %edge_cases = (
	name => [ '', 'a' x 1024, \"\x{263A}" ],
	age => [ -1, 0, 99999999 ],
);

# Takes a string input
our %edge_cases (
	'foo', 'bar'
);

    (Values can be strings or numbers; strings will be properly quoted.) Note that this only works with routines that take named parameters.

  • %type_edge_cases - optional hash mapping types to arrayrefs of extra values to try for any field of that type:

    our %type_edge_cases = (
	string => [ '', ' ', "\t", "\n", "\0", 'long' x 1024, chr(0x1F600) ],
	number => [ 0, 1.0, -1.0, 1e308, -1e308, 1e-308, -1e-308, 'NaN', 'Infinity' ],
	integer => [ 0, 1, -1, 2**31-1, -(2**31), 2**63-1, -(2**63) ],
);

  • %config - optional hash of configuration.

    The current supported variables are

    • test_nuls, inject NUL bytes into strings (default: 1)

    • test_undef, test with undefined value (default: 1)

    • test_empty, test with empty strings (default: 1)

    • dedup, fuzzing can create duplicate tests, go some way to remove duplicates (default: 1)

OUTPUT

The generated test:

  • Seeds RND (if configured) for reproducible fuzz runs

  • Uses edge cases (per-field and per-type) with configurable probability

  • Runs $iterations fuzz cases plus appended edge-case runs

  • Validates inputs with Params::Get / Params::Validate::Strict

  • Validates outputs with Return::Set

  • Runs static is(... ) corpus tests from Perl and/or YAML corpus

TRANSFORMS

Overview

Transforms allow you to define how input data should be transformed into output data. This is useful for testing functions that convert between formats, normalize data, or apply business logic transformations on a set of data to different set of data.

Transform schemas also have the keyword value, when a specific value is required

Configuration Example

---
module: Math::Utils
function: normalize_number

input:
  value:
    type: number
    position: 0

output:
  type: number

transforms:
  positive_stays_positive:
    input:
      value:
        type: number
        min: 0
        max: 1000
    output:
      type: number
      min: 0
      max: 1

  negative_becomes_zero:
    input:
      value:
        type: number
        max: 0
    output:
      type: number
      value: 0

  preserves_zero:
    input:
      value:
        type: number
        value: 0
    output:
      type: number
      value: 0

Transform Validation Rules

For each transform: 1. Generate test cases using the transform's input schema 2. Call the function with those inputs 3. Validate the output matches the transform's output schema 4. If output has a specific 'value', check exact match 5. If output has constraints (min/max), validate within bounds

EDGE CASE GENERATION

In addition to purely random fuzz cases, the harness generates deterministic edge cases for parameters that declare min, max or len in their schema definitions.

For each constraint, three edge cases are added:

  • Just inside the allowable range

    This case should succeed, since it lies strictly within the bounds.

  • Exactly on the boundary

    This case should succeed, since it meets the constraint exactly.

  • Just outside the boundary

    This case is annotated with _STATUS = 'DIES' in the corpus and should cause the harness to fail validation or croak.

Supported constraint types:

  • number, integer

    Uses numeric values one below, equal to, and one above the boundary.

  • string

    Uses strings of lengths one below, equal to, and one above the boundary.

  • arrayref

    Uses references to arrays of with the number of elements one below, equal to, and one above the boundary.

  • hashref

    Uses hashes with key counts one below, equal to, and one above the boundary (min = minimum number of keys, max = maximum number of keys).

  • memberof - arrayref of allowed values for a parameter

    This example is for a routine called input() that takes two arguments: status and level. status is a string that must have the value ok, error or pending. The level argument is an integer that must be one of 1, 5 or 111.

    ---
input:
  status:
    type: string
    memberof:
      - ok
      - error
      - pending
  level:
    type: integer
    memberof:
      - 1
      - 5
      - 111

    The generator will automatically create test cases for each allowed value (inside the member list), and at least one value outside the list (which should die or croak, _STATUS = 'DIES'). This works for strings, integers, and numbers.

  • boolean - automatic boundary tests for boolean fields

    input:
  flag:
    type: boolean

    The generator will automatically create test cases for 0 and 1; true and false; off and on, and values that should trigger _STATUS = 'DIES'.

These edge cases are inserted automatically, in addition to the random fuzzing inputs, so each run will reliably probe boundary conditions without relying solely on randomness.

EXAMPLES

Math::Simple::add()

Functional fuzz + Perl corpus + seed:

our $module = 'Math::Simple';
our $function = 'add';
our %input = ( a => { type => 'integer' }, b => { type => 'integer' } );
our %output = ( type => 'integer' );
our %cases = (
  '3' => [1, 2],
  '0' => [0, 0],
  '-1' => [-2, 1],
  '_STATUS:DIES' => [ 'a', 'b' ],     # non-numeric args should die
  '_STATUS:WARNS' => [ undef, undef ], # undef args should warn
);
our $seed = 12345;
our $iterations = 100;

Adding YAML file to generate tests

OO fuzz + YAML corpus + edge cases:

our %input = ( query => { type => 'string' } );
our %output = ( type => 'string' );
our $function = 'search';
our $new = { api_key => 'ABC123' };
our $yaml_cases = 't/corpus.yml';
our %edge_cases = ( query => [ '', '	', '<script>' ] );
our %type_edge_cases = ( string => [ \"\\0", "\x{FFFD}" ] );
our $seed = 999;

YAML Corpus Example (t/corpus.yml)

A YAML mapping of expected -> args array:

"success":
  - "Alice"
  - 30
"failure":
  - "Bob"

Example with arrayref + hashref

our %input = (
  tags => { type => 'arrayref', optional => 1 },
  config => { type => 'hashref' },
);
our %output = ( type => 'hashref' );

Example with memberof

our %input = (
    status => { type => 'string', memberof => [ 'ok', 'error', 'pending' ] },
);
our %output = ( type => 'string' );
our %config = ( test_nuls => 0, test_undef => 1 );

This will generate fuzz cases for 'ok', 'error', 'pending', and one invalid string that should die.

Adding Scheduled fuzz Testing with GitHub Actions to Your Code

To automatically create and run tests on a regular basis on GitHub Actions, you need to create a configuration file for each method and subroutine that you're testing, and a GitHub Actions configuration file.

This example takes you through testing the online_render method of HTML::Genealogy::Map.

t/conf/online_render.yml

---

module: HTML::Genealogy::Map
function: onload_render

input:
  gedcom:
    type: object
    can: individuals
  geocoder:
    type: object
    can: geocode
  debug:
    type: boolean
    optional: true
  google_key:
    type: string
    optional: true
    min: 39
    max: 39
    matches: "^AIza[0-9A-Za-z_-]{35}$"

config:
  test_undef: 0

.github/actions/fuzz.t

---
name: Fuzz Testing

permissions:
  contents: read

on:
  push:
    branches: [main, master]
  pull_request:
    branches: [main, master]
  schedule:
    - cron: '29 5 14 * *'

jobs:
  generate-fuzz-tests:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v5

      - name: Set up Perl
        uses: shogo82148/actions-setup-perl@v1
        with:
          perl-version: '5.42'

      - name: Install App::Test::Generator this module's dependencies
        run: |
          cpanm App::Test::Generator
          cpanm --installdeps .

      - name: Make Module
        run: |
          perl Makefile.PL
          make
        env:
          AUTOMATED_TESTING: 1

      - name: Generate fuzz tests
        run: |
          mkdir t/fuzz
          find t/conf -name '*.yml' | while read config; do
            test_name=$(basename "$config" .conf)
            fuzz-harness-generator "$config" > "t/fuzz/${test_name}_fuzz.t"
          done

      - name: Run generated fuzz tests
        run: |
          prove -lr t/fuzz/
        env:
          AUTOMATED_TESTING: 1

METHODS

generate($schema_file, $test_file)

Takes a schema file and produces a test file (or STDOUT).

NOTES

  • The legacy format conf file must use our declarations so variables are visible to the generator via require.

SEE ALSO

AUTHOR

Nigel Horne, <njh at nigelhorne.com>

Portions of this module's initial design and documentation were created with the assistance of ChatGPT (GPT-5), with final curation and authorship by Nigel Horne.