Metadata-Version: 2.4
Name: speclike
Version: 0.0.0.30
Summary: Partial support library for structured testing
Author: minoru_jp
License: MIT License
        
        Copyright (c) 2025 minoru_jp
        
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Project-URL: Homepage, https://github.com/minoru-jp/speclike
Project-URL: Source, https://github.com/minoru-jp/speclike
Project-URL: Issues, https://github.com/minoru-jp/speclike/issues
Keywords: spec,test,marker,pytest,structured,dbc,contract,asyncio
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: pytest
Requires-Dist: pytest>=7.0; extra == "pytest"
Dynamic: license-file

# speclike

`speclike` is a **pytest helper library** designed to define tests in a more structured and expressive way.  
It provides a declarative approach for building tests from two complementary perspectives:

- **Individual test bodies**, written as ordinary methods.
- **Externally defined dispatchers and actors**, representing scenario-driven or behavior-based tests.

The framework automatically generates executable pytest test functions (`test_...`) from decorated functions and classes.

---

## 🧩 Core Concepts

### 1. `Spec` and `ExSpec` Classes
- **`Spec`** — the main base class for declarative test specifications.  
  It manages auto-generated tests and delegates execution through `dispatch()` or `dispatch_async()`.
- **`ExSpec`** — groups externally defined dispatchers (functions that control test flow outside of the class).

Both are implemented using metaclasses (`_SpecMeta`, `_ExSpecMeta`) that synthesize pytest-compatible test functions during class creation.

---

### 2. `Case` and `Ex` Decorators
- **`Case`** — marks individual test bodies or actor functions (`def _(...):`) within a `Spec` class.  
  It can attach pytest marks, parametrize data, or skip tests dynamically.
- **`Ex`** — marks dispatcher functions used in `ExSpec` or top-level definitions.  
  Dispatchers define parameter structure using `PRM`, and connect to actors via `@case.ex(dispatcher)`.

---

### 3. `PRM` (Parameter Prefix Rules)
Defines how test parameters behave and interact between dispatcher and actor.

| Prefix | Kind | Behavior |
|---------|------|-----------|
| `_` | AO (Actor-Only) | Created by dispatcher, passed to actor (not parametrized) |
| *(none)* | AP (Actor-Parametrized) | Parametrized and passed to actor |
| `__` | PO (Param-Only) | Parametrized but **not** passed to actor (used for assertions) |

Parameter ordering must follow `AO → AP → PO`.

`PRM` validates actor signatures, generates pytest parametrization, and bridges runtime values through `_ParamsBridge`.

---

### 4. `Dispatcher` and `Actor`
- **Dispatcher**: a function decorated with `@ex` that defines test input combinations using `PRM`.
- **Actor**: a function named `_` decorated with `@case.ex(dispatcher)` that performs the actual behavior under test.
- The library automatically links each actor to its dispatcher and generates a `test_<dispatcher>` method that executes the pair.

---

### 5. Test Generation Workflow
1. The metaclass scans for decorated functions (`TargetKind`).
2. Each test body or dispatcher/actor pair is converted into a pytest-visible `test_...` function.
3. Signatures, parametrization, and pytest marks are copied to preserve readability and IDE support.
4. For external specs (`ExSpec`), tests are created dynamically based on defined dispatchers.

---

### 6. Highlights
- Strong signature validation for actors against their `PRM` definitions.
- Automatic propagation of `pytest.mark.parametrize` and other pytest marks.
- Source location (`co_firstlineno`) is preserved for accurate traceback references.
- Supports both sync and async test execution paths.

---

### Example

```python
from speclike import Spec, PRM

# Example domain object
class Context:
    def compute(self, x: int) -> int:
        return x * 10

# Get decorators
case, ex = Spec.get_decorators()

# Dispatcher (external). Parameters are NOT taken as direct function args.
# Access AP/PO values via the bridge `p`, and call the actor via `p.act(...)`.
@ex.follows(
    [(1, 10), (2, 20), (3, 30)],  # (value, __expected)
    ids=["x1", "x2", "x3"]
)
def check(p = PRM(_ctx=Context, value=int, __expected=int)):
    ctx = Context()                             # AO: create here
    result = p.act(_ctx=ctx, value=p.value)     # call actor with AO/AP
    assert result == p.__expected               # PO: only used in dispatcher

# Spec class with actor method named "_"
class TestCompute(Spec):
    @case.ex(check)
    def _(self, _ctx: Context, value: int) -> int:
        # Actor receives AO/AP only, in the declared order.
        return _ctx.compute(value)
````

At runtime, this generates:

* `test_check` — a parametrized pytest function executing the dispatcher.
* `act_for_check` — an internal bound actor function used by the dispatcher.

---

## Labeling Decorator (Case / Ex)

The library provides a hierarchical labeling mechanism applied through decorators such as:

```python
@case.api.input.default
@case.network.timeout
@case.tmp
```

Each decorator call selects one label from a navigable hierarchy:

```
Major → Intermediate → Minor
```

You may specify **0 to 3 labels**, and they can be combined simply by chaining attributes.
The resulting labels are gathered and stored inside a single pytest mark:

```python
pytest.mark.speclike("api", "input", "default")
```

The decorator ensures this mark is always attached cleanly to the function’s `pytestmark`.

---

### How the Decorator Behaves

When you write:

```python
@case.api.input.default
def check_something(): ...
```

the decorator:

1. Collects the labels `"api"`, `"input"`, `"default"`
2. Normalizes the target’s `pytestmark` into a list
3. Appends a `speclike(...)` mark containing those labels

The mark is purely declarative: each decorated function carries structured metadata describing its classification.

---

### Current Status of the `speclike` Marker

A pytest marker named **`speclike` is already defined**,
but **no runtime implementation, filtering logic, or pytest plugin behavior exists yet**.

At this stage:

* the marker is attached correctly
* pytest recognizes it as a registered mark
* **but it performs no special logic**
* filtering such as `-m "speclike"` is possible,
  yet argument-based filtering (`speclike("api")`) is **not implemented** until the plugin is created

Implementation is planned for future development.

---

### Why This Classification Helps

Even without a full plugin, the classification system already provides a strong structural benefit:

* labels consistently encode feature areas, scenarios, or test intent
* larger test suites become easier to navigate and reason about
* test naming and grouping become more predictable
* future tooling or plugins can rely on the embedded structure

Once the `speclike` implementation is added, these labels will support richer filtering, reporting, 
and domain-specific test behaviors—while keeping the decorator syntax compact and expressive.

---

Installation

pip
```bash
pip install speclike
```

github
```bash
pip install git+https://github.com/minoru-jp/speclike.git
```

---

## Status

This project is in **very early development (alpha stage)**.  
APIs and behavior may change without notice.

---

## License

MIT License © 2025 minoru_jp
