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rpa_vision_v3/tests/unit/test_analytics_vision_metrics.py
Dom af4ffa189a feat(analytics): normalise API + contrat explicite get_next_action (Lot A) 
Contrat get_next_action() — suppression du None ambigu :
  {"status": "selected", "edge": ..., ...}
  {"status": "terminal"}
  {"status": "blocked", "reason": "no_valid_edge" | ...}

ExecutionLoop dispatche proprement : blocked -> PAUSED + _pause_requested,
terminal -> succès légitime. Rétrocompat défensive (None legacy -> blocked).

Analytics API normalisée (kwargs-only) :
  on_execution_complete(duration_ms, status, steps_total|completed|failed)
  on_step_complete(duration_ms, ...)
  on_recovery_attempt(duration_ms, ...)

Découverte critique : les anciens appels utilisaient des méthodes et champs
inexistants (ExecutionMetrics.duration, metrics_collector.record_execution).
Le code n'avait jamais tourné au runtime — zéro analytics remontée.
L'exception était avalée par le try/except englobant.

58 tests (18 analytics + 11 contrat + 20 ExecutionLoop + 12 edge_scorer
non-régression). Migration complète, pas de pont legacy.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-15 09:06:19 +02:00

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"""
Tests unitaires pour la remontée des champs vision-aware (C1) vers analytics.
Couvre :
- StepMetrics.to_dict / from_dict avec les nouveaux champs
- AnalyticsExecutionIntegration.on_step_result passe bien les champs
- Persistance SQLite (schema + migration) des colonnes C1
"""
from __future__ import annotations
import sqlite3
import tempfile
from datetime import datetime
from pathlib import Path
from unittest.mock import MagicMock, patch
import pytest
from core.analytics.collection.metrics_collector import StepMetrics
# -----------------------------------------------------------------------------
# StepMetrics : sérialisation des champs C1
# -----------------------------------------------------------------------------
def _make_step_metrics(**overrides) -> StepMetrics:
base = dict(
step_id="s1",
execution_id="exec1",
workflow_id="wf1",
node_id="n1",
action_type="click",
target_element="",
started_at=datetime(2026, 4, 13, 10, 0, 0),
completed_at=datetime(2026, 4, 13, 10, 0, 1),
duration_ms=1000.0,
status="completed",
confidence_score=0.9,
retry_count=0,
error_details=None,
)
base.update(overrides)
return StepMetrics(**base)
class TestStepMetricsVisionFields:
def test_default_vision_fields(self):
m = _make_step_metrics()
assert m.ocr_ms == 0.0
assert m.ui_ms == 0.0
assert m.analyze_ms == 0.0
assert m.total_ms == 0.0
assert m.cache_hit is False
assert m.degraded is False
def test_to_dict_includes_vision_fields(self):
m = _make_step_metrics(
ocr_ms=120.5,
ui_ms=45.0,
analyze_ms=200.0,
total_ms=1050.0,
cache_hit=True,
degraded=True,
)
d = m.to_dict()
assert d["ocr_ms"] == 120.5
assert d["ui_ms"] == 45.0
assert d["analyze_ms"] == 200.0
assert d["total_ms"] == 1050.0
assert d["cache_hit"] is True
assert d["degraded"] is True
def test_from_dict_roundtrip(self):
original = _make_step_metrics(
ocr_ms=10.0, ui_ms=20.0, analyze_ms=30.0,
total_ms=100.0, cache_hit=True, degraded=False,
)
restored = StepMetrics.from_dict(original.to_dict())
assert restored.ocr_ms == 10.0
assert restored.ui_ms == 20.0
assert restored.analyze_ms == 30.0
assert restored.total_ms == 100.0
assert restored.cache_hit is True
assert restored.degraded is False
def test_from_dict_missing_vision_fields_defaults_to_zero(self):
"""Rétrocompatibilité : un dict sans champs C1 doit produire 0/False."""
restored = StepMetrics.from_dict({
'step_id': 's1',
'execution_id': 'e1',
'workflow_id': 'w1',
'node_id': 'n1',
'action_type': 'click',
'target_element': '',
'started_at': datetime.now().isoformat(),
'completed_at': datetime.now().isoformat(),
'duration_ms': 100.0,
'status': 'completed',
'confidence_score': 0.5,
})
assert restored.ocr_ms == 0.0
assert restored.cache_hit is False
assert restored.degraded is False
# -----------------------------------------------------------------------------
# AnalyticsExecutionIntegration.on_step_result
# -----------------------------------------------------------------------------
class _FakeStepResult:
"""Stand-in minimal pour core.execution.execution_loop.StepResult."""
def __init__(self, **kw):
self.success = kw.get("success", True)
self.node_id = kw.get("node_id", "n1")
self.edge_id = kw.get("edge_id", None)
self.action_result = kw.get("action_result", None)
self.match_confidence = kw.get("match_confidence", 0.9)
self.duration_ms = kw.get("duration_ms", 100.0)
self.message = kw.get("message", "")
self.ocr_ms = kw.get("ocr_ms", 0.0)
self.ui_ms = kw.get("ui_ms", 0.0)
self.analyze_ms = kw.get("analyze_ms", 0.0)
self.total_ms = kw.get("total_ms", 0.0)
self.cache_hit = kw.get("cache_hit", False)
self.degraded = kw.get("degraded", False)
class TestAnalyticsOnStepResult:
def test_on_step_result_passes_vision_fields(self):
from core.analytics.integration.execution_integration import (
AnalyticsExecutionIntegration,
)
# Analytics system mocké
fake_system = MagicMock()
integration = AnalyticsExecutionIntegration(fake_system)
step = _FakeStepResult(
node_id="node_click",
success=True,
match_confidence=0.87,
duration_ms=1234.0,
ocr_ms=111.0,
ui_ms=222.0,
analyze_ms=333.0,
total_ms=1234.0,
cache_hit=True,
degraded=False,
)
integration.on_step_result(
execution_id="exec1",
workflow_id="wf1",
step_result=step,
)
# Vérifie qu'un StepMetrics avec les bons champs a été enregistré
record_calls = fake_system.metrics_collector.record_step.call_args_list
assert len(record_calls) == 1
recorded: StepMetrics = record_calls[0].args[0]
assert isinstance(recorded, StepMetrics)
assert recorded.node_id == "node_click"
assert recorded.workflow_id == "wf1"
assert recorded.execution_id == "exec1"
assert recorded.confidence_score == 0.87
assert recorded.duration_ms == 1234.0
assert recorded.ocr_ms == 111.0
assert recorded.ui_ms == 222.0
assert recorded.analyze_ms == 333.0
assert recorded.total_ms == 1234.0
assert recorded.cache_hit is True
assert recorded.degraded is False
assert recorded.status == "completed"
def test_on_step_result_failed_step(self):
from core.analytics.integration.execution_integration import (
AnalyticsExecutionIntegration,
)
fake_system = MagicMock()
integration = AnalyticsExecutionIntegration(fake_system)
step = _FakeStepResult(
success=False,
message="Click failed",
degraded=True,
)
integration.on_step_result("e1", "w1", step)
recorded: StepMetrics = fake_system.metrics_collector.record_step.call_args.args[0]
assert recorded.status == "failed"
assert recorded.error_details == "Click failed"
assert recorded.degraded is True
def test_on_step_result_disabled_integration_is_noop(self):
from core.analytics.integration.execution_integration import (
AnalyticsExecutionIntegration,
)
integration = AnalyticsExecutionIntegration(None) # désactivé
assert integration.enabled is False
step = _FakeStepResult()
# Ne doit rien faire ni lever d'exception
integration.on_step_result("e1", "w1", step)
# -----------------------------------------------------------------------------
# AnalyticsExecutionIntegration.on_execution_complete (Lot A — avril 2026)
# -----------------------------------------------------------------------------
class TestAnalyticsOnExecutionComplete:
"""Contrat normalisé : duration_ms (ms) + status (str), pas de magie."""
def _make_integration(self):
from core.analytics.integration.execution_integration import (
AnalyticsExecutionIntegration,
)
fake_system = MagicMock()
# Pas d'execution active : l'intégration doit emprunter le fallback
# "ExecutionMetrics synthétique pushé dans _buffer".
fake_system.metrics_collector._active_executions = {}
fake_system.metrics_collector._lock = MagicMock()
fake_system.metrics_collector._lock.__enter__ = MagicMock(
return_value=None
)
fake_system.metrics_collector._lock.__exit__ = MagicMock(
return_value=None
)
fake_system.metrics_collector._buffer = []
return AnalyticsExecutionIntegration(fake_system), fake_system
def test_fallback_builds_execution_metrics_with_correct_fields(self):
"""Sans record_execution_start préalable, on construit un
ExecutionMetrics synthétique avec les bons noms de champs."""
from core.analytics.collection.metrics_collector import ExecutionMetrics
integration, fake_system = self._make_integration()
integration.on_execution_complete(
execution_id="exec1",
workflow_id="wf1",
duration_ms=1500.0,
status="completed",
steps_total=3,
steps_completed=3,
steps_failed=0,
)
# Un ExecutionMetrics a été pushé dans le buffer
buffer = fake_system.metrics_collector._buffer
assert len(buffer) == 1
metric: ExecutionMetrics = buffer[0]
assert isinstance(metric, ExecutionMetrics)
assert metric.execution_id == "exec1"
assert metric.workflow_id == "wf1"
assert metric.duration_ms == 1500.0
assert metric.status == "completed"
assert metric.steps_total == 3
assert metric.steps_completed == 3
assert metric.steps_failed == 0
# started_at / completed_at sont cohérents
delta_ms = (
metric.completed_at - metric.started_at
).total_seconds() * 1000
assert abs(delta_ms - 1500.0) < 1.0
def test_uses_record_execution_complete_if_active(self):
"""Si l'execution a été ouverte via on_execution_start, on délègue
à record_execution_complete (chemin nominal)."""
integration, fake_system = self._make_integration()
# Simuler une execution active
fake_system.metrics_collector._active_executions = {"exec1": object()}
integration.on_execution_complete(
execution_id="exec1",
workflow_id="wf1",
duration_ms=800.0,
status="failed",
steps_total=2,
steps_completed=1,
steps_failed=1,
error_message="timeout",
)
call = fake_system.metrics_collector.record_execution_complete.call_args
assert call is not None
kwargs = call.kwargs
assert kwargs["execution_id"] == "exec1"
assert kwargs["status"] == "failed"
assert kwargs["steps_total"] == 2
assert kwargs["steps_completed"] == 1
assert kwargs["steps_failed"] == 1
assert kwargs["error_message"] == "timeout"
def test_steps_total_derived_when_not_provided(self):
"""steps_total déduit par somme si absent, pas d'erreur silencieuse."""
integration, fake_system = self._make_integration()
integration.on_execution_complete(
execution_id="exec1",
workflow_id="wf1",
duration_ms=500.0,
status="completed",
steps_completed=2,
steps_failed=1,
)
metric = fake_system.metrics_collector._buffer[0]
assert metric.steps_total == 3 # 2 + 1
def test_disabled_integration_is_noop(self):
from core.analytics.integration.execution_integration import (
AnalyticsExecutionIntegration,
)
integration = AnalyticsExecutionIntegration(None)
assert integration.enabled is False
# Ne doit rien faire ni lever d'exception
integration.on_execution_complete(
execution_id="exec1",
workflow_id="wf1",
duration_ms=100.0,
status="completed",
)
def test_realtime_complete_called(self):
"""Le tracking temps réel est clos avec le bon status."""
integration, fake_system = self._make_integration()
integration.on_execution_complete(
execution_id="exec1",
workflow_id="wf1",
duration_ms=100.0,
status="stopped",
)
fake_system.realtime_analytics.complete_execution.assert_called_once_with(
execution_id="exec1",
status="stopped",
)
# -----------------------------------------------------------------------------
# AnalyticsExecutionIntegration.on_recovery_attempt (Lot A — avril 2026)
# -----------------------------------------------------------------------------
class TestAnalyticsOnRecoveryAttempt:
"""Contrat normalisé : StepMetrics construit avec les vrais champs."""
def test_success_recovery_builds_valid_step_metrics(self):
from core.analytics.collection.metrics_collector import StepMetrics
from core.analytics.integration.execution_integration import (
AnalyticsExecutionIntegration,
)
fake_system = MagicMock()
integration = AnalyticsExecutionIntegration(fake_system)
integration.on_recovery_attempt(
execution_id="exec1",
workflow_id="wf1",
node_id="node_click",
strategy="retry_with_delay",
success=True,
duration_ms=250.0,
)
call = fake_system.metrics_collector.record_step.call_args
assert call is not None
recorded: StepMetrics = call.args[0]
assert isinstance(recorded, StepMetrics)
assert recorded.execution_id == "exec1"
assert recorded.workflow_id == "wf1"
assert recorded.node_id == "node_click_recovery"
assert recorded.action_type == "recovery_retry_with_delay"
assert recorded.duration_ms == 250.0
assert recorded.status == "completed"
assert recorded.error_details is None
# Champs obligatoires du dataclass
assert recorded.step_id # non vide
assert recorded.target_element == ""
assert recorded.confidence_score == 0.0
def test_failed_recovery_sets_status_and_error_details(self):
from core.analytics.collection.metrics_collector import StepMetrics
from core.analytics.integration.execution_integration import (
AnalyticsExecutionIntegration,
)
fake_system = MagicMock()
integration = AnalyticsExecutionIntegration(fake_system)
integration.on_recovery_attempt(
execution_id="e1",
workflow_id="w1",
node_id="n1",
strategy="fallback_to_parent",
success=False,
duration_ms=80.0,
)
recorded: StepMetrics = (
fake_system.metrics_collector.record_step.call_args.args[0]
)
assert recorded.status == "failed"
assert recorded.error_details == "Recovery failed: fallback_to_parent"
assert recorded.duration_ms == 80.0
def test_disabled_integration_is_noop(self):
from core.analytics.integration.execution_integration import (
AnalyticsExecutionIntegration,
)
integration = AnalyticsExecutionIntegration(None)
integration.on_recovery_attempt(
execution_id="e1",
workflow_id="w1",
node_id="n1",
strategy="x",
success=True,
duration_ms=10.0,
)
# -----------------------------------------------------------------------------
# Persistance SQLite : schema + migration
# -----------------------------------------------------------------------------
class TestTimeSeriesStoreSchema:
def test_new_store_has_vision_columns(self, tmp_path):
from core.analytics.storage.timeseries_store import TimeSeriesStore
store = TimeSeriesStore(tmp_path)
with sqlite3.connect(str(store.db_path)) as conn:
cols = {row[1] for row in conn.execute(
"PRAGMA table_info(step_metrics)"
)}
# Colonnes legacy
assert "duration_ms" in cols
assert "confidence_score" in cols
# Colonnes C1
assert "ocr_ms" in cols
assert "ui_ms" in cols
assert "analyze_ms" in cols
assert "total_ms" in cols
assert "cache_hit" in cols
assert "degraded" in cols
def test_migration_adds_missing_columns(self, tmp_path):
"""Base pré-existante sans les colonnes C1 — la migration doit les ajouter."""
from core.analytics.storage.timeseries_store import TimeSeriesStore
# Créer une base "legacy" manuellement, sans les nouvelles colonnes
storage_dir = tmp_path / "legacy"
storage_dir.mkdir()
legacy_db = storage_dir / "timeseries.db"
with sqlite3.connect(str(legacy_db)) as conn:
conn.executescript("""
CREATE TABLE step_metrics (
step_id TEXT PRIMARY KEY,
execution_id TEXT NOT NULL,
workflow_id TEXT NOT NULL,
node_id TEXT NOT NULL,
action_type TEXT NOT NULL,
target_element TEXT,
started_at TIMESTAMP NOT NULL,
completed_at TIMESTAMP NOT NULL,
duration_ms REAL NOT NULL,
status TEXT NOT NULL,
confidence_score REAL,
retry_count INTEGER DEFAULT 0,
error_details TEXT
);
""")
conn.commit()
# Instancier TimeSeriesStore → doit migrer
_ = TimeSeriesStore(storage_dir)
with sqlite3.connect(str(legacy_db)) as conn:
cols = {row[1] for row in conn.execute(
"PRAGMA table_info(step_metrics)"
)}
assert "ocr_ms" in cols
assert "cache_hit" in cols
assert "degraded" in cols
def test_write_and_read_vision_metrics(self, tmp_path):
from core.analytics.storage.timeseries_store import TimeSeriesStore
store = TimeSeriesStore(tmp_path)
metric = _make_step_metrics(
ocr_ms=50.0, ui_ms=60.0, analyze_ms=110.0,
total_ms=500.0, cache_hit=True, degraded=True,
)
store.write_metrics([metric])
with sqlite3.connect(str(store.db_path)) as conn:
conn.row_factory = sqlite3.Row
row = conn.execute(
"SELECT * FROM step_metrics WHERE step_id = ?", (metric.step_id,)
).fetchone()
assert row is not None
assert row["ocr_ms"] == 50.0
assert row["ui_ms"] == 60.0
assert row["analyze_ms"] == 110.0
assert row["total_ms"] == 500.0
# SQLite stocke les bool comme INTEGER
assert row["cache_hit"] == 1
assert row["degraded"] == 1
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