Smoke Test Generator
VerifiedGenerate comprehensive API smoke test suites — categorised tests for auth, CRUD, integrations, cached vs live endpoints, with summary reporting. Use when val...
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# Smoke Test Generator
A structured pattern for API smoke testing with categorised test suites and summary reporting. Adapted from a production test suite that verified 34 endpoints across auth, CRUD, cached audio, story generation, ElevenLabs, and Mistral agent APIs.
Test Categories
| Category | What It Tests | Fail = | |---|---|---| | Auth | Login, token validation, protected routes | Nothing else works | | CRUD | Create, read, update, delete operations | Data layer broken | | Cached | Pre-cached content serves correctly | Demo will fail | | Live | Real API calls complete successfully | External dependency down | | Integration | End-to-end workflows across services | Pipeline broken |
Pattern
```python import httpx import asyncio
BASE_URL = "http://localhost:8000" results = {"pass": 0, "fail": 0, "skip": 0}
async def test(name: str, category: str, fn): try: await fn() results["pass"] += 1 print(f" ✅ [{category}] {name}") except Exception as e: results["fail"] += 1 print(f" ❌ [{category}] {name}: {e}")
async def run_smoke_tests(): async with httpx.AsyncClient(base_url=BASE_URL, timeout=30) as client: # Auth await test("Login with valid creds", "auth", lambda: assert_status(client.post("/login", json={"email": "[email protected]", "password": "test"}), 200)) # CRUD await test("Create item", "crud", lambda: assert_status(client.post("/api/items", json={"name": "test"}), 201)) # Cached await test("Cached content returns 200", "cached", lambda: assert_status(client.get("/api/cached/1"), 200)) # Integration await test("Full pipeline completes", "integration", lambda: assert_status(client.post("/api/pipeline", json={...}), 200)) total = results["pass"] + results["fail"] print(f"\n{'='*40}") print(f"Results: {results['pass']}/{total} passed") if results["fail"] > 0: print(f"⚠️ {results['fail']} failures — do not demo!") ```
Files
- `scripts/smoke_test.py` — Example smoke test suite with all categories
Use Cases
- Generate structured output from specifications or requirements
- Run automated tests to verify functionality and catch regressions
- Interact with external APIs for data retrieval and service integration
- Monitor and optimize API costs and token usage
- Generate smoke tests for rapid deployment verification
Pros & Cons
Pros
- +Clean CLI interface integrates well with automation pipelines and AI agents
- +Well-structured approach ensures consistent and reliable results
- +Integrates smoothly into existing workflows
Cons
- -Focused scope means it may not cover edge cases outside its primary use case
- -May require adaptation for non-standard project configurations
FAQ
What does Smoke Test Generator do?
What platforms support Smoke Test Generator?
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