https://github.com/hiro-o918/fauxgen
A factory method generator that streamlines test data creation by automating field value generation.
https://github.com/hiro-o918/fauxgen
python testing
Last synced: 16 days ago
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A factory method generator that streamlines test data creation by automating field value generation.
- Host: GitHub
- URL: https://github.com/hiro-o918/fauxgen
- Owner: hiro-o918
- Created: 2025-03-24T12:03:36.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-05-06T03:57:20.000Z (17 days ago)
- Last Synced: 2025-05-06T04:33:46.654Z (17 days ago)
- Topics: python, testing
- Language: Rust
- Homepage: https://pypi.org/project/fauxgen/
- Size: 239 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
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README
# fauxgen
A factory method generator that streamlines test data creation by automating field value generation.
## Overview
Testing classes with numerous fields often requires extensive boilerplate code. `fauxgen` addresses this challenge by automatically generating factory methods from class definitions, significantly reducing test code complexity.
Currently supports `pandera.DataFrameModel` with plans for future expansions.
## Quick Start
```bash
uv add fauxgen
uv run fauxgen gen --module-dir
```
## Usage Example
Example project can be found in the [examples](./examples/).
### Traditional Approach (Without fauxgen)
Testing DataFrame models traditionally requires explicit specification of all fields, even when testing a single validation:
```python
import pandera as pa
from pandera.typing import Series
class UserSchema(pa.DataFrameModel):
id: Series[int] = pa.Field(ge=1)
age: Series[int] = pa.Field(ge=0, le=150)
name: Series[str] = pa.Field()
email: Series[str] = pa.Field(nullable=True)
active: Series[bool] = pa.Field()
def test_user_registration():
# Forced to specify every field when testing age validation
df_user = pd.DataFrame([
{
"id": 1, # Unrelated to test
"age": 151, # Actual test target
"name": "test", # Unrelated to test
"email": "[email protected]", # Unrelated to test
"active": True, # Unrelated to test
},
]).pipe(DataFrame[UserSchema])
# Test age validation...
```
### Simplified Testing (With fauxgen)
fauxgen generates factory methods that enable focused testing by automatically handling irrelevant fields:
```python
from .factories import user_schema_record # Generated by fauxgen
def test_user_registration():
# Focus solely on the field under test
# Other fields are automatically populated with valid values
df_user = pd.DataFrame([
user_schema_record(age=151), # Set specific age for test
]).pipe(DataFrame[UserSchema]) # Test age validation...
def test_user_email_optional():
# Effortlessly test specific scenarios
# without worrying about irrelevant fields
df_user = pd.DataFrame([
user_schema_record(email=None) # Set email to None for test
]).pipe(DataFrame[UserSchema]) # Test nullable email...
```
## How It Works
### Input: pandera.DataFrameModel Definition
```python
import pandera as pa
from pandera.typing import Series
class TestSchema(pa.DataFrameModel):
int_col: Series[int] = pa.Field(ge=0.1, le=10.0)
float_col_only_ge: Series[float] = pa.Field(ge=10)
float_col_only_le: Series[float] = pa.Field(le=10)
pa_bool_col: Series[pa.Bool] = pa.Field(nullable=True)
```
### Output: Generated Factory Method
```python
import fauxgen as f
class TestSchemaRecord(TypedDict):
int_col: int
float_col_only_ge: float
float_col_only_le: float
pa_bool_col: bool | None
def test_schema_record(
*,
int_col: int | f.Unset = f.UNSET,
float_col_only_ge: float | f.Unset = f.UNSET,
float_col_only_le: float | f.Unset = f.UNSET,
pa_bool_col: bool | None | f.Unset = f.UNSET,
seed_: int | None = None,
) -> TestSchemaRecord:
return {
"int_col": f.Unset.unwrap_or_else(int_col, lambda: f.gen_int(ge=0.1, le=10, seed=seed_)),
"float_col_only_ge": f.Unset.unwrap_or_else(float_col_only_ge, lambda: f.gen_float(ge=10, le=110, seed=seed_)),
"float_col_only_le": f.Unset.unwrap_or_else(float_col_only_le, lambda: f.gen_float(ge=-90, le=10, seed=seed_)),
"pa_bool_col": f.Unset.unwrap_or_else(pa_bool_col, lambda: f.gen_bool(seed=seed_)),
}
```
### Key Features
1. **Enhanced Type Safety**:
- Leverages TypedDict for comprehensive type checking
- Provides full IDE support with type hints and autocompletion
2. **Intelligent Field Generation**:
- Selectively override specific fields while auto-generating others
- Maintains data integrity through validation-aware value generation
3. **Validation-Aware Generation**:
- Automatically respects field constraints (`ge`, `le`, etc.)
- Properly handles optional fields with `nullable` support