Pipelines de tests autoreparables

Las suites de tests tradicionales se rompen cuando cambia la UI. Los tests autoreparables usan la memoria de Synapse para aprender de fallos pasados y adaptarse — reduciendo tests inestables y la carga de mantenimiento.

Concepto

┌─────────┐  fails   ┌──────────┐  store   ┌──────────┐
│  Test   │ ───────▶ │  Synapse │ ───────▶ │ Memories │
│  Run    │          │  Memory  │          │ (failures)│
└─────────┘          └──────────┘          └──────────┘
                           ▲                     │
                           │   recall            │
                           │  before next run    │
                           └─────────────────────┘

1. El test se ejecuta
2. Si falla, almacena el fallo (qué salió mal, por qué, cómo arreglarlo)
3. Próxima ejecución: recupera los fallos relevantes antes de ejecutar
4. Aplica las correcciones conocidas automáticamente

Implementación

Paso 1: Wrapper de test

Envuelva cada test con recall/store de memoria:

import requests
from datetime import datetime

URL = "https://synapse.schaefer.zone"
MIND_KEY = "mk_..."

def self_healing_test(test_name, test_fn):
    """Decorator: wrap a test with self-healing memory."""
    def wrapper():
        # 1. Recall past failures for this test
        past_failures = requests.get(
            f"{URL}/memory/search?q={test_name}+failure",
            headers={"Authorization": f"Bearer {MIND_KEY}"}
        ).json()
        
        # 2. Run test with failure context
        try:
            test_fn(known_failures=past_failures)
        except Exception as e:
            # 3. Store the failure
            store_failure(test_name, e, traceback.format_exc())
            raise
    
    return wrapper

def store_failure(test_name, error, traceback_str):
    requests.post(f"{URL}/memory",
        headers={"Authorization": f"Bearer {MIND_KEY}",
                 "Content-Type": "application/json"},
        json={
            "category": "mistake",
            "key": f"test_failure_{test_name}_{datetime.now().isoformat()}",
            "content": f"Test: {test_name}\nError: {error}\nTrace:\n{traceback_str}",
            "tags": ["test", "failure", test_name],
            "priority": "high"
        })

Paso 2: Lógica de test adaptativa

Dentro del test, compruebe si hay fallos conocidos y aplique correcciones:

@self_healing_test
def test_login_page(browser, known_failures=None):
    browser.goto("https://app.com/login")
    
    # Check if we've seen this page change before
    if known_failures and known_failures.get("results"):
        for failure in known_failures["results"]:
            if "button moved" in failure["content"].lower():
                # Use accessibility label instead of coordinates
                browser.click(by_label="Login button")
                return
    
    # Default: use coordinates
    browser.click(x=150, y=400)

Paso 3: Estrategias de recuperación

Almacene estrategias de recuperación como memorias:

def store_recovery(failure_type, strategy):
    requests.post(f"{URL}/memory",
        headers={"Authorization": f"Bearer {MIND_KEY}",
                 "Content-Type": "application/json"},
        json={
            "category": "skill",
            "key": f"recovery_{failure_type}",
            "content": strategy,
            "tags": ["test", "recovery", failure_type],
            "priority": "high"
        })

# Store recoveries for common failures
store_recovery("element_not_found",
    "When element not found by ID, try by CSS class, then by XPath, "
    "then by accessibility label. Take screenshot for debugging.")

store_recovery("timeout",
    "Increase timeout to 30s. If still fails, check if page is loading "
    "dynamically — wait for specific element instead of fixed time.")

store_recovery("stale_element",
    "Re-find element before each interaction. Don't cache element references "
    "across page transitions.")

Paso 4: Integración con CI

# .gitlab-ci.yml
test:self-healing:
  script:
    - export SYNAPSE_MIND_KEY=$SYNAPSE_TEST_MIND_KEY
    - pytest tests/ --self-healing
  after_script:
    # Summarize new failures
    - python scripts/synapse_failure_summary.py

Paso 5: Dashboard de análisis de fallos

# Get all test failures from the last week
r = requests.get(
    f"{URL}/memory/search?q=test+failure",
    headers={"Authorization": f"Bearer {MIND_KEY}"}
)

# Group by test name
failures = {}
for mem in r.json().get("results", []):
    test_name = extract_test_name(mem["content"])
    failures.setdefault(test_name, []).append(mem)

# Report
for test, fails in sorted(failures.items(), key=lambda x: -len(x[1])):
    print(f"{test}: {len(fails)} failures")

Mejores prácticas

Patrones de fallo comunes a almacenar

Próximos pasos

• Pruebas automatizadas en iOS
• Mejores prácticas de memoria
• Cookbook de recuperación de errores