PropWise
An AST-based analyzer for identifying property-based testing candidates in Elixir codebases.
Overview
PropWise analyzes your Elixir code to find functions that would benefit from property-based testing. It examines the Abstract Syntax Tree (AST) of your code to:
- Detect pure functions (functions without side effects)
- Identify common patterns suitable for property testing
- Find inverse function pairs (encode/decode, serialize/deserialize, etc.)
- Score and rank candidates by testability
- Provide specific testing suggestions for each candidate
- Rank findings meaningfully (inverse pairs first) and show only the top 3 by
default, with
--show-allto expand
Features
Purity Analysis
Detects side effects by analyzing function calls:
- I/O operations (File, IO)
- Process operations (GenServer, Agent, Task)
- Database operations (Ecto)
- HTTP requests
- System calls
- Message passing
Pattern Detection
Identifies functions with characteristics ideal for property testing:
- Collection Operations: Functions using Enum, Stream, or list comprehensions
- Data Transformations: Struct update and map write operations
- Validation Functions: Functions following naming conventions (
?suffix,valid/check/is_prefix) - Algebraic Structures: Merge, concat, union, compose, and other potentially algebraic operations
- Encoders/Decoders: Serialization and encoding/decoding functions
- Numeric Algorithms:
:mathmodule calls, kernel numeric functions, and significant arithmetic (2+ operations)
Inverse Pair Detection
Finds function pairs that are inverses of each other:
- encode/decode
- serialize/deserialize
- parse/format or parse/generate
- compress/decompress
- encrypt/decrypt
- to_/from_
- pack/unpack
- marshal/unmarshal
Concrete Test Generation
Generates ready-to-use property-based test code:
- Supports multiple libraries:
stream_data(default) andPropEr - Specific test properties tailored to detected patterns
- Complete test blocks with appropriate generators
- Assertions matching the function's expected behavior
- Copy-paste ready test code to get started quickly
Installation
As a Library
Add propwise to your list of dependencies in mix.exs:
def deps do
[
{:propwise, "~> 0.2"}
]
end
As a Command-Line Tool
Option 1: escript (Recommended for standalone use)
Build and install the standalone executable:
cd propwise
mix deps.get
mix escript.build
# Copy to a directory in your PATH
sudo cp propwise /usr/local/bin/
# Or just use it directly
./propwise
The escript bundles all dependencies and works without Mix or any additional setup.
Option 2: Mix archive
Install globally from Hex as a Mix archive:
mix archive.install hex propwise
This makes the mix propwise task available in any project. Note: This requires jason to be available in your Mix environment.
To uninstall:
mix archive.uninstall propwise
Option 3: As a dependency
When added as a project dependency, PropWise provides a Mix task:
mix propwise
Usage
Command Line
Using escript
# Analyze current project
./propwise .
# Analyze with custom minimum score
./propwise --min-score 5 ./my_project
# Output as JSON
./propwise --format json ./my_project
# Use PropEr instead of stream_data
./propwise --library proper ./my_project
# Show all findings instead of just the top 3
./propwise --show-all ./my_project
# Show help
./propwise --help
Using Mix task
# Analyze current project
mix propwise
# Analyze with custom minimum score
mix propwise --min-score 5
# Output as JSON
mix propwise --format json
# Use PropEr instead of stream_data
mix propwise --library proper
# Show all findings instead of just the top 3
mix propwise --show-all
# Analyze another project
mix propwise ../other_project
# Show help
mix propwise --help
As a Library
# Analyze a project
result = PropWise.analyze("./my_project")
# Analyze with custom options
result = PropWise.analyze("./my_project", min_score: 5, library: :proper)
# Print the report
PropWise.print_report(result)
# Print as JSON
PropWise.print_report(result, format: :json)
Analyzing a Subset of Files (for Pull Requests)
PropWise supports analyzing specific files or file subsets. This is ideal for PR workflows to focus analysis only on modified or newly added code.
-
Single File:
mix propwise lib/my_app/user.ex # or ./propwise lib/my_app/user.ex -
Comma-Separated File List (
--filesflag):mix propwise --files "lib/my_app/user.ex,lib/my_app/auth.ex" -
Multiple File Path Arguments:
mix propwise lib/my_app/user.ex lib/my_app/auth.ex
CI / PR Integration
You can easily integrate PropWise into your CI pipeline to run property testing candidate detection on Pull Requests:
Bash / CI Pipeline Script
# Get modified or added .ex files relative to main branch
CHANGED_FILES=$(git diff --name-only origin/main...HEAD | grep '\.ex$' | paste -sd,)
if [ -n "$CHANGED_FILES" ]; then
mix propwise --no-fail --files "$CHANGED_FILES"
else
echo "No Elixir files modified in this PR."
fi
GitHub Actions Example (.github/workflows/propwise.yml)
name: PropWise PR Analysis
on:
pull_request:
branches: [ main ]
jobs:
analyze-pr:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- uses: erlef/setup-beam@v1
with:
elixir-version: '1.18'
otp-version: '27'
- run: mix deps.get
- name: Run PropWise on PR Changed Files
run: |
CHANGED_FILES=$(git diff --name-only origin/main...HEAD | grep '\.ex$' | paste -sd,)
if [ -n "$CHANGED_FILES" ]; then
mix propwise --no-fail --files "$CHANGED_FILES"
else
echo "No Elixir files modified in this PR."
fi
Example Output
By default, PropWise shows the top 3 ranked candidates and inverse pairs, then a
… and N more note. Pass --show-all to see everything.
PropWise Analysis Report
Summary
▸ Total functions analyzed: 143
▸ Property test candidates: 24
▸ Candidates dropped (below threshold): 12
▸ Coverage: 16.8%
Inverse Function Pairs Detected
1. `MyApp.Encoder.encode/1` <-> `decode/1`
- Suggestion: Test round-trip property: decode(encode(x)) == x
Candidates (ranked)
1. MyApp.Encoder.encode/1
▸ Rank: #1
▸ Score: 4
▸ Location: lib/my_app/encoder.ex:12
▸ Type: public
▸ Patterns:
▸ Encoder/Decoder: Encoding/decoding function
▸ Testing suggestions:
property "encode/decode round-trip" do
check all(data <- term()) do
encoded = Encoder.encode(data)
assert Encoder.decode(encoded) == {:ok, data}
end
end
2. MyApp.Encoder.decode/1
▸ Rank: #2
▸ Score: 4
▸ Location: lib/my_app/encoder.ex:18
▸ Type: public
▸ Patterns:
▸ Encoder/Decoder: Encoding/decoding function
▸ Testing suggestions:
...
3. MyApp.List.merge_sorted/2
▸ Rank: #3
▸ Score: 8
▸ Location: lib/my_app/list.ex:15
▸ Type: public
▸ Patterns:
▸ Collection Operation: Uses Enum collection operations
▸ Algebraic Structure: Potentially algebraic operation
▸ Testing suggestions:
...
… and 21 more (run with `--show-all` to see all findings).
Note how
encode/decoderank abovemerge_sortedeven though they have a lower score: inverse-pair members are the most actionable findings, so they are always ranked first.
Scoring System
Functions are scored based on multiple factors:
- Base score: 1 point for pure functions
- Pattern detection: 2 points per detected pattern
- Multiple patterns: 2 bonus points for functions with 2+ patterns
- Complexity: 1 bonus point for non-trivial functions
- Visibility: 1 bonus point for public functions
Default minimum score is 4, but this can be adjusted based on your needs.
For detailed information about all detection criteria and scoring rules, see Scoring.
Configuration
You can customize PropWise's behavior by creating a .propwise.exs file in your project root.
Example Configuration
# .propwise.exs
%{
# Directories to analyze (relative to project root)
# Default: ["lib"]
analyze_paths: ["lib"],
# Property-based testing library to use for suggestions
# Options: :stream_data (default) or :proper
library: :stream_data,
# Modules whose functions are known to be pure. Calls into these modules do
# not downgrade the caller to :unknown. Accepts module atoms or strings.
# Default: []
pure_modules: [MyApp.Math, MyApp.Parsing]
# You can analyze multiple directories:
# analyze_paths: ["lib", "src", "apps/my_app/lib"]
}
Configuration Options
analyze_paths- List of directories to analyze relative to project root (default:["lib"])library- Property testing library for code generation::stream_dataor:proper(default::stream_data)pure_modules- Modules whose functions are known to be pure (default:[])
If no .propwise.exs file is present, PropWise will use the defaults.
Options
CLI Options
-m, --min-score NUM: Minimum score for candidates (default: 4)-f, --format FORMAT: Output format: text or json (default: text)-o, --output FILE: Write output to file instead of stdout-l, --library LIB: Property testing library: stream_data or proper (default: stream_data)--files FILES: Comma-separated list of files to analyze (ideal for PRs)--baseline FILE: Only report candidates that are new or whose score increased relative to the given baseline file (CI ratcheting)--write-baseline FILE: Write a baseline snapshot of the current candidates and exit--show-all: Show all findings instead of just the top 3 (still ranked)--no-fail: Exit with code 0 even when suggestions are found-h, --help: Show help message
Note: CLI options override configuration file settings.
Library Options
:min_score- Minimum score threshold (integer, default: 4):format- Output format (:textor:json, default::text):library- Property testing library (:stream_dataor:proper, default::stream_data):show_all- Show all findings instead of the top 3 (boolean, default:false):limit- Override the default display limit of 3 (integer; ignored when:show_allistrue)
Ranking and Output Volume
PropWise reports can be noisy on larger codebases. To keep them actionable, findings are ranked and truncated by default:
- Every candidate is assigned a 1-based rank (shown as
Rank: #Nand as a heading prefix). - Ranking prioritizes, in order:
- Inverse-pair members (e.g.
encode/decode) — round-trip properties are the highest-value tests, so these always rank above other candidates regardless of score. - Higher score.
- Public functions before private ones.
- A stable alphabetical tie-break.
- Inverse-pair members (e.g.
- By default only the top 3 candidates and top 3 inverse pairs are shown, each
followed by a
… and N morenote. - Pass
--show-allto display every finding (still ranked).
# Default: top 3 findings only
mix propwise
# Show every finding, still ranked
mix propwise --show-all
How It Works
- Parse: Recursively finds all
.exfiles in configured directories (default:lib) - Extract: Parses each file's AST and extracts function definitions
- Analyze Purity: Walks the AST to detect side effects
- Detect Patterns: Looks for common patterns in function structure and naming
- Score: Calculates a testability score for each function
- Find Pairs: Identifies inverse function pairs across the codebase
- Generate Suggestions: Creates concrete property-based test examples using your chosen library
- Report: Presents findings with ready-to-use test code
Purity: three states, not two
A binary pure/impure split forces every function that calls any other function
to be guessed at. MyApp.Helpers.normalize/1 might be a pure list transform or
might hit the database — static analysis cannot know without following the call.
PropWise therefore classifies each function as one of:
| State | Meaning |
|---|---|
:pure |
Every call in the body resolves to a known-pure function |
:unknown |
No side effect found, but some calls could not be resolved |
:impure |
A known side effect was found |
:unknown candidates are still reported, but scored at half weight and
ranked below :pure ones — so genuinely clean functions come first while
nothing is silently discarded. Reports name the unresolved calls, so you can
see why a function is uncertain:
- **Purity:** unknown (unresolved calls)
- **Unresolved calls:** is_map, merge_configs
Declaring your own pure modules
If your codebase has modules you know are pure, tell PropWise and their callers
will be classified :pure instead of :unknown:
# .propwise.exs
%{
analyze_paths: ["lib"],
pure_modules: [MyApp.Math, MyApp.Parsing]
}
Inferred laws: which property should I write?
Pattern detection answers "is this worth testing?". The law table answers the much more useful question: which property should I write?
PropWise inspects the functions a body actually calls and infers algebraic laws
from a curated table. A function that calls Map.merge/3 is reported as
associative, idempotent, and having an identity element — not the generic
"deterministic output" that applies to literally any pure function:
- **Inferred laws:** idempotence, associativity, identity element, commutativity
Those laws become real, ready-to-run properties:
property "associativity: f(f(a, b), c) == f(a, f(b, c))" do
check all(a <- term(), b <- term(), c <- term()) do
assert MyApp.Config.merge_configs(
MyApp.Config.merge_configs(a, b), c
) ==
MyApp.Config.merge_configs(
a, MyApp.Config.merge_configs(b, c)
)
end
end
property "identity element: f(a, identity) == a" do
check all(a <- term()) do
identity = %{}
assert MyApp.Config.merge_configs(a, identity) == a
end
end
Idempotence is rendered arity-appropriately: f(f(x)) == f(x) for a unary
function, f(a, a) == a for a binary operation (which is exactly what
Map.merge(m, m) == m expresses).
The table covers ~60 functions across maps (Map.merge, Map.put, …),
collections (Enum.reverse → involution, Enum.sort → idempotence +
permutation + monotonicity, …), strings (String.trim → idempotence,
String.split → round-trip), numerics (abs → idempotent + monotonic), and
encodings (Base.encode64, Jason.encode, :erlang.term_to_binary →
round-trip). See PropWise.LawTable for the full list and the law vocabulary.
Baseline mode: ratchet your CI
A report you read once is a report you ignore. Baseline mode turns PropWise into a ratchet: accept today's state, then fail only when a change makes things worse.
# 1. Snapshot the current state (commit this file)
mix propwise --write-baseline .propwise/baseline.json
# 2. In CI: report only newly introduced candidates
mix propwise --baseline .propwise/baseline.json
- Candidates already in the baseline are suppressed.
- A candidate whose score increased counts as a regression and is reported.
- A candidate whose score decreased is an improvement and stays suppressed.
- Exit code is
1only when there are new/regressed candidates, so the check passes on unchanged code and fails on new debt.
GitHub Actions example:
- name: PropWise ratchet
run: mix propwise --baseline .propwise/baseline.json
A malformed or unreadable baseline is a hard error rather than a silent empty baseline — otherwise every existing candidate would look new and flood CI.
Limitations
- Static analysis only - doesn't execute code
- Purity is three-state but still heuristic: a function calling an unresolved
module is
:unknown(half weight), not silently assumed pure. Addpure_modulesto sharpen it. - Pattern detection is heuristic-based
- Law inference is table-driven: it recognises the ~60 listed functions, not arbitrary code
- Doesn't analyze macros or dynamically generated code in depth
- Inverse-pair detection is name-based and limited to functions defined in the same module
Installation Notes
marcli (used to render the text report with ANSI colours) is an optional
dependency. Without it, PropWise prints raw Markdown instead — everything else
works identically.
marcli pulls in mdex_native, a Rust NIF. config/config.exs forces a
from-source build of that NIF by default, because the precompiled artifact
download fails behind some proxies. If your network can reach GitHub releases,
set PROPWISE_NO_NATIVE_BUILD=1 before compiling for a much faster build.
Security Note
PropWise loads configuration from .propwise.exs files using Code.eval_file/1,
which executes arbitrary Elixir code. Only analyze projects you trust.
Function names are interned with String.to_existing_atom/1, so analysing
untrusted source cannot exhaust the VM's atom table.
Contributing
Contributions are welcome! Areas for improvement:
- Additional pattern detectors
- Smarter purity analysis (tracking function calls across modules)
- Integration with existing property testing libraries
- IDE integration
License
MIT