feat(perf): Add performance tests and high-precision timing

tests/test_perf_dag.py

@@ -0,0 +1,75 @@
+import pytest
+from src.models import Ticket
+from src.dag_engine import TrackDAG
+from src.performance_monitor import get_monitor
+
+def test_dag_performance():
+ perf = get_monitor()
+ perf.enabled = True
+ 
+ num_tickets = 500
+ tickets = []
+ # Create a complex DAG: linear dependencies + branching
+ for i in range(num_tickets):
+  deps = []
+  if i > 0:
+   # Linear dependency
+   deps.append(f"ticket_{i-1}")
+  if i > 10:
+   # Branching dependency (binary tree-ish)
+   deps.append(f"ticket_{i//2}")
+  if i > 50:
+   # More cross-links
+   deps.append(f"ticket_{i-10}")
+  
+  tickets.append(Ticket(
+   id=f"ticket_{i}",
+   description=f"Ticket number {i}",
+   depends_on=deps,
+   status="todo"
+  ))
+ 
+ dag = TrackDAG(tickets[::-1])
+ 
+ # 1. Measure has_cycle()
+ cycle_found = dag.has_cycle()
+ assert cycle_found is False
+ 
+ # 2. Measure topological_sort()
+ sorted_ids = dag.topological_sort()
+ assert len(sorted_ids) == num_tickets
+ 
+ # 3. Measure cascade_blocks()
+ tickets[0].status = "blocked"
+ dag.cascade_blocks()
+ 
+ # Verify all are blocked
+ for t in tickets:
+  assert t.status == "blocked", f"Ticket {t.id} failed to cascade block"
+ 
+ # 4. Verify metrics were recorded
+ metrics = perf.get_metrics()
+ assert "time_dag_has_cycle_ms" in metrics
+ assert "time_dag_topological_sort_ms" in metrics
+ assert "time_dag_cascade_blocks_ms" in metrics
+ 
+ # Output results for visibility when running with -s
+ print(f"\n[PERF] DAG Performance (n={num_tickets}):")
+ print(f"  has_cycle:        {metrics['time_dag_has_cycle_ms']:.4f} ms")
+ print(f"  topological_sort: {metrics['time_dag_topological_sort_ms']:.4f} ms")
+ print(f"  cascade_blocks:   {metrics['time_dag_cascade_blocks_ms']:.4f} ms")
+
+def test_dag_edge_cases():
+ # Test cycle detection correctness
+ t1 = Ticket(id="a", description="a", depends_on=["b"])
+ t2 = Ticket(id="b", description="b", depends_on=["c"])
+ t3 = Ticket(id="c", description="c", depends_on=["a"])
+ dag = TrackDAG([t1, t2, t3])
+ assert dag.has_cycle() is True
+ with pytest.raises(ValueError, match="Dependency cycle detected"):
+  dag.topological_sort()
+ 
+ # Test empty DAG
+ dag_empty = TrackDAG([])
+ assert dag_empty.has_cycle() is False
+ assert dag_empty.topological_sort() == []