專業化 EA 資源壓力告警

tests/test_elephant_alpha_engine.py

@@ -159,3 +159,124 @@ def test_escalate_resource_optimization_without_evidence_is_suppressed(monkeypat
 
     assert cooldown == ["resource_optimization"]
     assert suppressed == [("resource_optimization", "no_concrete_evidence")]
+
+
+def test_resource_pressure_classifier_does_not_equate_backlog_with_cpu_load():
+    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
+
+    metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
+        "action_queue_size": 34,
+        "high_priority_count": 0,
+        "human_review_count": 0,
+        "stale_count": 0,
+        "system_load_pct": 19.2,
+        "queue_threshold": 10,
+        "load_threshold_pct": 80,
+        "high_priority_threshold": 5,
+        "stale_threshold": 5,
+    })
+
+    assert metrics["pressure_level"] == "backlog_only"
+    assert metrics["should_alert"] is False
+    assert metrics["load_pressure"] is False
+    assert "system_load=19.2%/80%" in metrics["evidence"]
+
+
+def test_resource_pressure_classifier_alerts_on_actionable_review_backlog():
+    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
+
+    metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
+        "action_queue_size": 34,
+        "high_priority_count": 8,
+        "human_review_count": 6,
+        "stale_count": 0,
+        "system_load_pct": 22.0,
+        "queue_threshold": 10,
+        "load_threshold_pct": 80,
+        "high_priority_threshold": 5,
+        "stale_threshold": 5,
+    })
+
+    assert metrics["pressure_level"] == "warning"
+    assert metrics["should_alert"] is True
+    assert metrics["high_priority_pressure"] is True
+    assert metrics["load_pressure"] is False
+
+
+def test_resource_pressure_message_is_measurement_based_not_llm_theatre():
+    from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
+
+    metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
+        "action_queue_size": 34,
+        "high_priority_count": 8,
+        "human_review_count": 6,
+        "stale_count": 5,
+        "system_load_pct": 22.0,
+        "queue_threshold": 10,
+        "load_threshold_pct": 80,
+        "high_priority_threshold": 5,
+        "stale_threshold": 5,
+        "stale_hours": 24,
+    })
+
+    msg = ElephantAlphaAutonomousEngine._build_resource_pressure_telegram_message(
+        metrics,
+        insight_id=123,
+        previous_limit=10,
+        new_limit=8,
+    )
+
+    assert "量測指標" in msg
+    assert "主機 CPU 未達高負載門檻" in msg
+    assert "未啟動 Hermes/NemoTron 價格分析" in msg
+    assert "預期效益" not in msg
+    assert "48小時" not in msg
+    assert "48 小時效益預測" in msg
+
+
+def test_resource_optimization_bypasses_llm_orchestrator(monkeypatch):
+    import services.elephant_alpha_autonomous_engine as engine_module
+    from services.elephant_alpha_autonomous_engine import (
+        AutonomousTrigger,
+        ElephantAlphaAutonomousEngine,
+    )
+
+    engine = ElephantAlphaAutonomousEngine()
+    sent = []
+    stored = []
+
+    async def _boom(*args, **kwargs):
+        raise AssertionError("resource_optimization must not call LLM orchestrator")
+
+    async def _capture_send(*args, **kwargs):
+        sent.append(args)
+
+    monkeypatch.setattr(engine_module.elephant_orchestrator, "analyze_and_coordinate", _boom)
+    monkeypatch.setattr(engine, "_record_resource_pressure_insight", lambda *args, **kwargs: 77)
+    monkeypatch.setattr(engine, "_send_resource_pressure_telegram", _capture_send)
+    monkeypatch.setattr(engine, "_store_escalation", lambda trigger_type: stored.append(trigger_type))
+
+    trigger = AutonomousTrigger(
+        trigger_type="resource_optimization",
+        conditions={
+            "_resource_metrics": {
+                "action_queue_size": 34,
+                "high_priority_count": 9,
+                "human_review_count": 6,
+                "stale_count": 0,
+                "system_load_pct": 20.0,
+                "queue_threshold": 10,
+                "load_threshold_pct": 80,
+                "high_priority_threshold": 5,
+                "stale_threshold": 5,
+            }
+        },
+        threshold=0.6,
+        enabled=True,
+    )
+
+    asyncio.run(engine._execute_autonomous_decision(trigger))
+
+    assert stored == ["resource_optimization"]
+    assert len(sent) == 1
+    assert engine.max_autonomous_decisions_per_hour == 8