Property-Based Testing in Elixir with StreamData

Traditional example-based tests verify behavior against a handful of specific inputs. Property-based tests describe invariants that must hold for any valid input, then let the framework generate hundreds of random test cases to find violations. This post covers practical usage of ExUnitProperties and StreamData in the Prismatic Platform.

Why Property-Based Testing Matters

Consider a URL validator. An example-based test might check:

assert Validator.valid_url?("https://example.com")
refute Validator.valid_url?("not a url")

This covers two cases. A property-based test covers hundreds:

property "all generated valid URLs pass validation" do
check all scheme <- member_of(["http", "https"]),
host <- string(:alphanumeric, min_length: 1, max_length: 63),
tld <- member_of(["com", "org", "net", "io"]),
path <- string(:alphanumeric, min_length: 0, max_length: 50) do
url = "#{scheme}://#{host}.#{tld}/#{path}"
assert Validator.valid_url?(url)
end
end

Each test run generates 100 random combinations by default. Over time, this explores far more of the input space than hand-written examples ever could.

Setting Up StreamData

Add the dependency to your mix.exs:

defp deps do
[
{:stream_data, "~> 1.0", only: [:dev, :test]}
]
end

In your test files:

defmodule MyApp.ValidatorPropertyTest do
use ExUnit.Case, async: true
use ExUnitProperties


# Properties go here
end

Core Generators

StreamData provides generators for common types. These are the building blocks:

GeneratorOutputExample

|---|---|---|

integer()Any integer-42, 0, 1337 positive_integer()Integers > 01, 999 float()Any float-3.14, 0.0, 1.5e10 string(:alphanumeric)Alphanumeric strings"abc123" binary()Raw binaries<<1, 2, 3>> boolean()true or falsetrue atom(:alphanumeric)Random atoms:abc list_of(generator)Lists of generated values[1, 3, 7] map_of(key_gen, val_gen)Maps%{"a" => 1} member_of(enumerable)One element from list"active" from ["active", "inactive"] one_of(generators)Value from one generatorMixed types

Building Custom Generators

Real-world testing needs domain-specific generators. Here is a generator for a DD case entity as used in the platform:

defmodule Prismatic.Generators do
@moduledoc """
StreamData generators for Prismatic domain types.
"""
use ExUnitProperties


@spec dd_case_params() :: StreamData.t(map())
def dd_case_params do
gen all name <- string(:alphanumeric, min_length: 3, max_length: 100),
status <- member_of([:draft, :active, :completed, :archived]),
priority <- member_of([:low, :medium, :high, :critical]),
entity_count <- integer(0..50),
confidence <- float(min: 0.0, max: 1.0) do
%{
name: name,
status: status,
priority: priority,
entity_count: entity_count,
confidence_score: Float.round(confidence, 4)
}
end
end


@spec email_address() :: StreamData.t(String.t())
def email_address do
gen all local <- string(:alphanumeric, min_length: 1, max_length: 64),
domain <- string(:alphanumeric, min_length: 1, max_length: 63),
tld <- member_of(["com", "org", "net", "io", "cz"]) do
"#{local}@#{domain}.#{tld}"
end
end


@spec ico_number() :: StreamData.t(String.t())
def ico_number do
gen all digits <- list_of(integer(0..9), length: 8) do
Enum.join(digits)
end
end
end

Real Property Examples

Roundtrip Properties

The most powerful property pattern: encode then decode should return the original value.

property "JSON roundtrip preserves DD case data" do
check all params <- Prismatic.Generators.dd_case_params() do
encoded = Jason.encode!(params)
decoded = Jason.decode!(encoded, keys: :atoms)


assert decoded.name == params.name
assert decoded.status == Atom.to_string(params.status)
assert decoded.entity_count == params.entity_count
end
end


property "Base64 roundtrip preserves binary data" do
check all data <- binary(min_length: 0, max_length: 10_000) do
assert data == data |> Base.encode64() |> Base.decode64!()
end
end

Invariant Properties

These assert conditions that must always be true regardless of input:

property "confidence scores are always between 0 and 1" do
check all raw_score <- float(min: -100.0, max: 100.0) do
normalized = Prismatic.DD.ScoringEngine.normalize_confidence(raw_score)
assert normalized >= 0.0
assert normalized <= 1.0
end
end


property "sanitized strings never contain script tags" do
check all input <- string(:printable, max_length: 500) do
sanitized = Prismatic.Sanitizer.strip_html(input)
refute String.contains?(sanitized, "<script")
refute String.contains?(sanitized, "javascript:")
end
end

Commutative / Associative Properties

Mathematical properties that should hold for domain operations:

property "merging entity lists is associative" do
check all a <- list_of(entity_gen(), max_length: 20),
b <- list_of(entity_gen(), max_length: 20),
c <- list_of(entity_gen(), max_length: 20) do
left = EntityMerger.merge(EntityMerger.merge(a, b), c)
right = EntityMerger.merge(a, EntityMerger.merge(b, c))


assert MapSet.new(left, & &1.id) == MapSet.new(right, & &1.id)
end
end

Idempotency Properties

Operations that should produce the same result when applied multiple times:

property "deduplication is idempotent" do
check all items <- list_of(string(:alphanumeric), max_length: 100) do
once = Prismatic.Utils.deduplicate(items)
twice = Prismatic.Utils.deduplicate(once)
assert once == twice
end
end

Shrinking

When StreamData finds a failing case, it shrinks the input to the smallest value that still triggers the failure. This is automatic and makes debugging far easier.

For example, if a list of 47 elements triggers a bug, StreamData will try progressively smaller lists until it finds the minimal failing case, often a list of 1 or 2 elements.

Custom generators built with gen all get shrinking for free. If you build generators using StreamData.bind/2 or StreamData.map/2, shrinking propagates through the composition.

# This generator automatically shrinks each component independently
gen all name <- string(:alphanumeric, min_length: 1),
age <- positive_integer(),
tags <- list_of(atom(:alphanumeric), max_length: 5) do
%{name: name, age: age, tags: tags}
end

If a test fails for %{name: "xQ7", age: 42, tags: [:a, :b]}, shrinking might reduce it to %{name: "a", age: 1, tags: [:a]}.

Integration with TACH Doctrine

The Prismatic Platform's TACH doctrine mandates property-based tests for pure/stateless modules: validators, parsers, encoders, scoring functions. The enforcement checks for ExUnitProperties usage in test files corresponding to these module types:

# TACH audit checks for property test coverage
mix tach.audit --property-gaps

Modules flagged as pure (no side effects, no GenServer state, no database access) that lack property tests appear as advisory warnings during CI.

Configuring Test Runs

Control the number of generated cases per property:

# In test_helper.exs or per-test
@tag property_iterations: 500


property "holds for many inputs", %{property_iterations: n} do
check all input <- integer(), max_runs: n do
assert is_integer(input)
end
end

For CI, we run 200 iterations. For local development, 100 is the default. For pre-release validation, 1000.

Common Pitfalls

Overly constrained generators defeat the purpose. If your generator only produces values that your code handles correctly, you are testing nothing. Include edge cases: empty strings, zero, negative numbers, Unicode.

Slow generators can make property tests impractical. Avoid database calls inside generators. Properties should test pure logic.

Flaky properties from non-deterministic behavior (timestamps, random values inside the function under test) need careful handling. Inject dependencies rather than relying on system state.

Summary

PatternUse CaseExample

|---|---|---|

RoundtripEncode/decode, serialize/deserializeJSON, Base64, protocol buffers InvariantOutput constraints, bounds, sanitizationConfidence scores, HTML stripping CommutativeOrder-independent operationsSet operations, merges IdempotentRepeated application safetyDeduplication, normalization OracleCompare against reference implementationNew parser vs regex, optimized vs naive

Property-based testing finds bugs that example-based tests miss. Combined with the TACH doctrine's enforcement, it forms a safety net that grows stronger with every test run.