767 lines
18 KiB
Markdown
767 lines
18 KiB
Markdown
# Testing Guide - Theory and Best Practices
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## Table of Contents
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- [Introduction](#introduction)
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- [Your Current Approach vs. Test-Driven Development](#your-current-approach-vs-test-driven-development)
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- [The Testing Pyramid](#the-testing-pyramid)
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- [Key Concepts](#key-concepts)
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- [Real-World Testing Workflow](#real-world-testing-workflow)
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- [Regression Testing](#regression-testing---the-killer-feature)
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- [Code Coverage](#coverage---how-much-is-tested)
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- [Best Practices](#best-practices-summary)
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- [Practical Examples](#practical-example-your-workflow)
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- [When Should You Write Tests](#when-should-you-write-tests)
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- [Getting Started](#your-next-steps)
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---
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## Introduction
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This guide explains the theory and best practices of software testing, specifically for the PIF Compiler project. It moves beyond ad-hoc testing scripts to a comprehensive, automated testing approach.
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---
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## Your Current Approach vs. Test-Driven Development
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### What You Do Now (Ad-hoc Scripts):
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```python
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# test_script.py
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from cosing_service import cosing_search
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result = cosing_search("WATER", mode="name")
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print(result) # Look at output, check if it looks right
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```
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**Problems:**
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- ❌ Manual checking (is the output correct?)
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- ❌ Not repeatable (you forget what "correct" looks like)
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- ❌ Doesn't catch regressions (future changes break old code)
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- ❌ No documentation (what should the function do?)
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- ❌ Tedious for many functions
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---
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## The Testing Pyramid
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```
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/\
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/ \ E2E Tests (Few)
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/----\
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/ \ Integration Tests (Some)
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/--------\
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/ \ Unit Tests (Many)
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/____________\
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```
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### 1. **Unit Tests** (Bottom - Most Important)
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Test individual functions in isolation.
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**Example:**
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```python
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def test_parse_cas_numbers_single():
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"""Test parsing a single CAS number."""
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result = parse_cas_numbers(["7732-18-5"])
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assert result == ["7732-18-5"] # ← Automated check
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```
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**Benefits:**
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- ✅ Fast (milliseconds)
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- ✅ No external dependencies (no API, no database)
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- ✅ Pinpoint exact problem
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- ✅ Run hundreds in seconds
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**When to use:**
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- Testing individual functions
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- Testing data parsing/validation
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- Testing business logic calculations
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---
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### 2. **Integration Tests** (Middle)
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Test multiple components working together.
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**Example:**
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```python
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def test_full_cosing_workflow():
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"""Test search + clean workflow."""
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raw = cosing_search("WATER", mode="name")
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clean = clean_cosing(raw)
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assert "cosingUrl" in clean
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```
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**Benefits:**
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- ✅ Tests real interactions
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- ✅ Catches integration bugs
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**Drawbacks:**
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- ⚠️ Slower (hits real APIs)
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- ⚠️ Requires internet/database
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**When to use:**
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- Testing workflows across multiple services
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- Testing API integrations
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- Testing database interactions
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---
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### 3. **E2E Tests** (End-to-End - Top - Fewest)
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Test entire application flow (UI → Backend → Database).
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**Example:**
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```python
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def test_create_pif_from_ui():
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"""User creates PIF through Streamlit UI."""
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# Click buttons, fill forms, verify PDF generated
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```
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**When to use:**
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- Testing complete user workflows
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- Smoke tests before deployment
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- Critical business processes
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---
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## Key Concepts
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### 1. **Assertions - Automated Verification**
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**Old way (manual):**
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```python
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result = parse_cas_numbers(["7732-18-5/56-81-5"])
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print(result) # You look at: ['7732-18-5', '56-81-5']
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# Is this right? Maybe? You forget in 2 weeks.
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```
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**Test way (automated):**
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```python
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def test_parse_multiple_cas():
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result = parse_cas_numbers(["7732-18-5/56-81-5"])
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assert result == ["7732-18-5", "56-81-5"] # ← Computer checks!
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# If wrong, test FAILS immediately
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```
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**Common Assertions:**
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```python
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# Equality
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assert result == expected
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# Truthiness
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assert result is not None
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assert "key" in result
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# Exceptions
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with pytest.raises(ValueError):
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invalid_function()
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# Approximate equality (for floats)
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assert result == pytest.approx(3.14159, rel=1e-5)
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```
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---
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### 2. **Mocking - Control External Dependencies**
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**Problem:** Testing `cosing_search()` hits the real COSING API:
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- ⚠️ Slow (network request)
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- ⚠️ Unreliable (API might be down)
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- ⚠️ Expensive (rate limits)
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- ⚠️ Hard to test errors (how do you make API return error?)
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**Solution: Mock it!**
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```python
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from unittest.mock import Mock, patch
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@patch('cosing_service.req.post') # Replace real HTTP request
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def test_search_by_name(mock_post):
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# Control what the "API" returns
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mock_response = Mock()
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mock_response.json.return_value = {
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"results": [{"metadata": {"inciName": ["WATER"]}}]
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}
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mock_post.return_value = mock_response
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result = cosing_search("WATER", mode="name")
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assert result["inciName"] == ["WATER"] # ← Test your logic, not the API
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mock_post.assert_called_once() # Verify it was called
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```
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**Benefits:**
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- ✅ Fast (no real network)
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- ✅ Reliable (always works)
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- ✅ Can test error cases (mock API failures)
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- ✅ Isolate your code from external issues
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**What to mock:**
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- HTTP requests (`requests.get`, `requests.post`)
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- Database calls (`db.find_one`, `db.insert`)
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- File I/O (`open`, `read`, `write`)
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- External APIs (COSING, ECHA, PubChem)
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- Time-dependent functions (`datetime.now()`)
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---
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### 3. **Fixtures - Reusable Test Data**
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**Without fixtures (repetitive):**
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```python
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def test_clean_basic():
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data = {"inciName": ["WATER"], "casNo": ["7732-18-5"], ...}
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result = clean_cosing(data)
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assert ...
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def test_clean_empty():
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data = {"inciName": ["WATER"], "casNo": ["7732-18-5"], ...} # Copy-paste!
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result = clean_cosing(data)
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assert ...
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```
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**With fixtures (DRY - Don't Repeat Yourself):**
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```python
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# conftest.py
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@pytest.fixture
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def sample_cosing_response():
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"""Reusable COSING response data."""
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return {
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"inciName": ["WATER"],
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"casNo": ["7732-18-5"],
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"substanceId": ["12345"]
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}
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# test file
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def test_clean_basic(sample_cosing_response): # Auto-injected!
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result = clean_cosing(sample_cosing_response)
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assert result["inciName"] == "WATER"
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def test_clean_empty(sample_cosing_response): # Reuse same data!
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result = clean_cosing(sample_cosing_response)
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assert "cosingUrl" in result
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```
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**Benefits:**
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- ✅ No code duplication
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- ✅ Centralized test data
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- ✅ Easy to update (change once, affects all tests)
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- ✅ Auto-cleanup (fixtures can tear down resources)
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**Common fixture patterns:**
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```python
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# Database fixture with cleanup
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@pytest.fixture
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def test_db():
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db = connect_to_test_db()
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yield db # Test runs here
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db.drop_all() # Cleanup after test
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# Temporary file fixture
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@pytest.fixture
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def temp_file(tmp_path):
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file_path = tmp_path / "test.json"
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file_path.write_text('{"test": "data"}')
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return file_path # Auto-cleaned by pytest
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```
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---
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## Real-World Testing Workflow
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### Scenario: You Add a New Feature
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**Step 1: Write the test FIRST (TDD - Test-Driven Development):**
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```python
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def test_parse_cas_removes_parentheses():
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"""CAS numbers with parentheses should be cleaned."""
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result = parse_cas_numbers(["7732-18-5 (hydrate)"])
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assert result == ["7732-18-5"]
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```
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**Step 2: Run test - it FAILS (expected!):**
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```bash
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$ uv run pytest tests/test_cosing_service.py::test_parse_cas_removes_parentheses
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FAILED: AssertionError: assert ['7732-18-5 (hydrate)'] == ['7732-18-5']
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```
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**Step 3: Write code to make it pass:**
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```python
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def parse_cas_numbers(cas_string: list) -> list:
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cas_string = cas_string[0]
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cas_string = re.sub(r"\([^)]*\)", "", cas_string) # ← Add this
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# ... rest of function
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```
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**Step 4: Run test again - it PASSES:**
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```bash
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$ uv run pytest tests/test_cosing_service.py::test_parse_cas_removes_parentheses
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PASSED ✓
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```
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**Step 5: Refactor if needed - tests ensure you don't break anything!**
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---
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### TDD Cycle (Red-Green-Refactor)
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```
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1. RED: Write failing test
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↓
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2. GREEN: Write minimal code to pass
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↓
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3. REFACTOR: Improve code without breaking tests
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↓
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Repeat
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```
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**Benefits:**
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- ✅ Forces you to think about requirements first
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- ✅ Prevents over-engineering
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- ✅ Built-in documentation (tests show intended behavior)
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- ✅ Confidence to refactor
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---
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## Regression Testing - The Killer Feature
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**Scenario: You change code 6 months later:**
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```python
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# Original (working)
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def parse_cas_numbers(cas_string: list) -> list:
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cas_string = cas_string[0]
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cas_string = re.sub(r"\([^)]*\)", "", cas_string)
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cas_parts = re.split(r"[/;,]", cas_string) # Handles /, ;, ,
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return [cas.strip() for cas in cas_parts]
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# You "improve" it
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def parse_cas_numbers(cas_string: list) -> list:
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return cas_string[0].split("/") # Simpler! But...
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```
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**Run tests:**
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```bash
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$ uv run pytest
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FAILED: test_multiple_cas_with_semicolon
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Expected: ['7732-18-5', '56-81-5']
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Got: ['7732-18-5;56-81-5'] # ← Oops, broke semicolon support!
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FAILED: test_cas_with_parentheses
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Expected: ['7732-18-5']
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Got: ['7732-18-5 (hydrate)'] # ← Broke parentheses removal!
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```
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**Without tests:**
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- You deploy
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- Users report bugs
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- You're confused what broke
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- Spend hours debugging
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**With tests:**
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- Instant feedback
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- Fix before deploying
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- Save hours of debugging
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---
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## Coverage - How Much Is Tested?
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### Running Coverage
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```bash
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uv run pytest --cov=src/pif_compiler --cov-report=html
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```
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### Sample Output
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```
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Name Stmts Miss Cover
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--------------------------------------------------
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cosing_service.py 89 5 94%
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echa_service.py 156 89 43%
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models.py 45 45 0%
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--------------------------------------------------
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TOTAL 290 139 52%
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```
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### Interpretation
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- ✅ `cosing_service.py` - **94% covered** (great!)
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- ⚠️ `echa_service.py` - **43% covered** (needs more tests)
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- ❌ `models.py` - **0% covered** (no tests yet)
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### Coverage Goals
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| Coverage | Status | Action |
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|----------|--------|--------|
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| 90-100% | ✅ Excellent | Maintain |
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| 70-90% | ⚠️ Good | Add edge cases |
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| 50-70% | ⚠️ Acceptable | Prioritize critical paths |
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| <50% | ❌ Poor | Add tests immediately |
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**Target:** 80%+ for business-critical code
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### HTML Coverage Report
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```bash
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uv run pytest --cov=src/pif_compiler --cov-report=html
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# Open htmlcov/index.html in browser
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```
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Shows:
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- Which lines are tested (green)
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- Which lines are not tested (red)
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- Which branches are not covered
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---
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## Best Practices Summary
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### ✅ DO:
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1. **Write tests for all business logic**
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```python
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# YES: Test calculations
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def test_sed_calculation():
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ingredient = Ingredient(quantity=10.0, dap=0.5)
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assert ingredient.calculate_sed() == 5.0
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```
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2. **Mock external dependencies**
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```python
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# YES: Mock API calls
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@patch('cosing_service.req.post')
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def test_search(mock_post):
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mock_post.return_value.json.return_value = {...}
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```
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3. **Test edge cases**
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```python
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# YES: Test edge cases
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def test_parse_empty_cas():
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assert parse_cas_numbers([""]) == []
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def test_parse_invalid_cas():
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with pytest.raises(ValueError):
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parse_cas_numbers(["abc-def-ghi"])
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```
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4. **Keep tests simple**
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```python
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# YES: One test = one thing
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def test_cas_removes_whitespace():
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assert parse_cas_numbers([" 123-45-6 "]) == ["123-45-6"]
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# NO: Testing multiple things
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def test_cas_everything():
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assert parse_cas_numbers([" 123-45-6 "]) == ["123-45-6"]
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assert parse_cas_numbers(["123-45-6/789-01-2"]) == [...]
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# Too much in one test!
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```
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5. **Run tests before committing**
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```bash
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git add .
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uv run pytest # ← Always run first!
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git commit -m "Add feature X"
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```
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6. **Use descriptive test names**
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```python
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# YES: Describes what it tests
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def test_parse_cas_removes_parenthetical_info():
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...
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# NO: Vague
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def test_cas_1():
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...
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```
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---
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### ❌ DON'T:
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1. **Don't test external libraries**
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```python
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# NO: Testing if requests.post works
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def test_requests_library():
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response = requests.post("https://example.com")
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assert response.status_code == 200
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# YES: Test YOUR code that uses requests
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@patch('requests.post')
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def test_my_search_function(mock_post):
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...
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```
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2. **Don't make tests dependent on each other**
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```python
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# NO: test_b depends on test_a
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def test_a_creates_data():
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db.insert({"id": 1, "name": "test"})
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def test_b_uses_data():
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data = db.find_one({"id": 1}) # Breaks if test_a fails!
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# YES: Each test is independent
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def test_b_uses_data():
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db.insert({"id": 1, "name": "test"}) # Create own data
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data = db.find_one({"id": 1})
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```
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3. **Don't test implementation details**
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```python
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# NO: Testing internal variable names
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def test_internal_state():
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obj = MyClass()
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assert obj._internal_var == "value" # Breaks with refactoring
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# YES: Test public behavior
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def test_public_api():
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obj = MyClass()
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assert obj.get_value() == "value"
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```
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4. **Don't skip tests**
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```python
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# NO: Commenting out failing tests
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# def test_broken_feature():
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# assert broken_function() == "expected"
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# YES: Fix the test or mark as TODO
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@pytest.mark.skip(reason="Feature not implemented yet")
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def test_future_feature():
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...
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```
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---
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## Practical Example: Your Workflow
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### Before (Manual Script)
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```python
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# test_water.py
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from cosing_service import cosing_search, clean_cosing
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result = cosing_search("WATER", "name")
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print(result) # ← You manually check
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clean = clean_cosing(result)
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print(clean) # ← You manually check again
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# Run 10 times with different inputs... tedious!
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```
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**Problems:**
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- Manual verification
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- Slow (type command, read output, verify)
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- Error-prone (miss things)
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- Not repeatable
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---
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### After (Automated Tests)
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```python
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# tests/test_cosing_service.py
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def test_search_and_clean_water():
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"""Water should be searchable and cleanable."""
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result = cosing_search("WATER", "name")
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assert result is not None
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assert "inciName" in result
|
||
|
||
clean = clean_cosing(result)
|
||
assert clean["inciName"] == "WATER"
|
||
assert "cosingUrl" in clean
|
||
|
||
# Run ONCE: pytest
|
||
# It checks everything automatically!
|
||
```
|
||
|
||
**Run all 25 tests:**
|
||
```bash
|
||
$ uv run pytest
|
||
|
||
tests/test_cosing_service.py::TestParseCasNumbers::test_single_cas_number PASSED
|
||
tests/test_cosing_service.py::TestParseCasNumbers::test_multiple_cas_with_slash PASSED
|
||
...
|
||
======================== 25 passed in 0.5s ========================
|
||
```
|
||
|
||
**Benefits:**
|
||
- ✅ All pass? Safe to deploy!
|
||
- ❌ One fails? Fix before deploying!
|
||
- ⏱️ 25 tests in 0.5 seconds vs. manual testing for 30 minutes
|
||
|
||
---
|
||
|
||
## When Should You Write Tests?
|
||
|
||
### Always Test:
|
||
|
||
✅ **Business logic** (calculations, data processing)
|
||
```python
|
||
# YES
|
||
def test_calculate_sed():
|
||
assert calculate_sed(quantity=10, dap=0.5) == 5.0
|
||
```
|
||
|
||
✅ **Data validation** (Pydantic models)
|
||
```python
|
||
# YES
|
||
def test_ingredient_validates_cas_format():
|
||
with pytest.raises(ValidationError):
|
||
Ingredient(cas="invalid", quantity=10.0)
|
||
```
|
||
|
||
✅ **API integrations** (with mocks)
|
||
```python
|
||
# YES
|
||
@patch('requests.post')
|
||
def test_cosing_search(mock_post):
|
||
...
|
||
```
|
||
|
||
✅ **Bug fixes** (write test first, then fix)
|
||
```python
|
||
# YES
|
||
def test_bug_123_empty_cas_crash():
|
||
"""Regression test for bug #123."""
|
||
result = parse_cas_numbers([]) # Used to crash
|
||
assert result == []
|
||
```
|
||
|
||
---
|
||
|
||
### Sometimes Test:
|
||
|
||
⚠️ **UI code** (harder to test, less critical)
|
||
```python
|
||
# Streamlit UI tests are complex, lower priority
|
||
```
|
||
|
||
⚠️ **Configuration** (usually simple)
|
||
```python
|
||
# Config loading is straightforward, test if complex logic
|
||
```
|
||
|
||
---
|
||
|
||
### Don't Test:
|
||
|
||
❌ **Third-party libraries** (they have their own tests)
|
||
```python
|
||
# NO: Testing if pandas works
|
||
def test_pandas_dataframe():
|
||
df = pd.DataFrame({"a": [1, 2, 3]})
|
||
assert len(df) == 3 # Pandas team already tested this!
|
||
```
|
||
|
||
❌ **Trivial code**
|
||
```python
|
||
# NO: Testing simple getters/setters
|
||
class MyClass:
|
||
def get_name(self):
|
||
return self.name # Too simple to test
|
||
```
|
||
|
||
---
|
||
|
||
## Your Next Steps
|
||
|
||
### 1. Install Pytest
|
||
|
||
```bash
|
||
cd c:\Users\adish\Projects\pif_compiler
|
||
uv add --dev pytest pytest-cov pytest-mock
|
||
```
|
||
|
||
### 2. Run the COSING Tests
|
||
|
||
```bash
|
||
# Run all tests
|
||
uv run pytest
|
||
|
||
# Run with verbose output
|
||
uv run pytest -v
|
||
|
||
# Run specific test file
|
||
uv run pytest tests/test_cosing_service.py
|
||
|
||
# Run specific test
|
||
uv run pytest tests/test_cosing_service.py::TestParseCasNumbers::test_single_cas_number
|
||
```
|
||
|
||
### 3. See Coverage
|
||
|
||
```bash
|
||
# Terminal report
|
||
uv run pytest --cov=src/pif_compiler/services/cosing_service
|
||
|
||
# HTML report (more detailed)
|
||
uv run pytest --cov=src/pif_compiler --cov-report=html
|
||
# Open htmlcov/index.html in browser
|
||
```
|
||
|
||
### 4. Start Writing Tests for New Code
|
||
|
||
Follow the TDD cycle:
|
||
1. **Red**: Write failing test
|
||
2. **Green**: Write minimal code to pass
|
||
3. **Refactor**: Improve code
|
||
4. Repeat!
|
||
|
||
---
|
||
|
||
## Additional Resources
|
||
|
||
### Pytest Documentation
|
||
- [Official Pytest Docs](https://docs.pytest.org/)
|
||
- [Pytest Fixtures](https://docs.pytest.org/en/stable/fixture.html)
|
||
- [Pytest Mocking](https://docs.pytest.org/en/stable/monkeypatch.html)
|
||
|
||
### Testing Philosophy
|
||
- [Test-Driven Development (TDD)](https://www.freecodecamp.org/news/test-driven-development-what-it-is-and-what-it-is-not-41fa6bca02a2/)
|
||
- [Testing Best Practices](https://testautomationuniversity.com/)
|
||
- [The Testing Pyramid](https://martinfowler.com/articles/practical-test-pyramid.html)
|
||
|
||
### PIF Compiler Specific
|
||
- [tests/README.md](../tests/README.md) - Test suite documentation
|
||
- [tests/RUN_TESTS.md](../tests/RUN_TESTS.md) - Quick start guide
|
||
- [REFACTORING.md](../REFACTORING.md) - Code organization changes
|
||
|
||
---
|
||
|
||
## Summary
|
||
|
||
**Testing transforms your development workflow:**
|
||
|
||
| Without Tests | With Tests |
|
||
|---------------|------------|
|
||
| Manual verification | Automated checks |
|
||
| Slow feedback | Instant feedback |
|
||
| Fear of breaking things | Confidence to refactor |
|
||
| Undocumented behavior | Tests as documentation |
|
||
| Debug for hours | Pinpoint issues immediately |
|
||
|
||
**Start small:**
|
||
1. Write tests for one service (✅ COSING done!)
|
||
2. Add tests for new features
|
||
3. Fix bugs with tests first
|
||
4. Gradually increase coverage
|
||
|
||
**The investment pays off:**
|
||
- Fewer bugs in production
|
||
- Faster development (less debugging)
|
||
- Better code design
|
||
- Easier collaboration
|
||
- Peace of mind 😌
|
||
|
||
---
|
||
|
||
*Last updated: 2025-01-04*
|