fix(exec-dashboard): replace time-dependent throughput with Pipeline Conversion %

'Validation Throughput (tests/week)' was time-dependent — director wanted
an activity-based metric instead.

New metric: Pipeline Conversion Rate
  formula: validated / (validated + rejected + in_review) × 100
  unit: %  (no time reference)
  meaning: 'of all tests that have entered validation, X% succeeded'
  trend: declining if in_review backlog > validated count,
         improving if conversion ≥ 80%, stable otherwise

Backend: calculate_validation_throughput() rewritten — same API key
(tests_per_week) kept for compatibility, new conversion_rate field added.
Frontend: label → 'Pipeline Conversion', unit → '%', tooltip updated.

backend/app/services/operational_metrics_service.py

@@ -237,45 +237,59 @@ def calculate_coverage_velocity(db: Session) -> dict:
 
 
 def calculate_validation_throughput(db: Session) -> dict:
-    """Calculate tests validated/rejected per week."""
-    twelve_weeks_ago = datetime.utcnow() - timedelta(weeks=12)
+    """Pipeline Conversion Rate — activity-based, no time dependency.
 
-    # Tests validated
-    validated_weekly = (
-        db.query(
-            func.date_trunc("week", Test.red_validated_at).label("week"),
-            func.count(Test.id).label("count"),
-        )
-        .filter(
-            Test.red_validated_at >= twelve_weeks_ago,
-            Test.state.in_([TestState.validated, TestState.rejected]),
-        )
-        .group_by(func.date_trunc("week", Test.red_validated_at))
-        .order_by("week")
-        .all()
+    Measures what percentage of tests that have entered the validation
+    phase have been successfully approved (validated).
+
+    formula: validated / (validated + rejected + in_review) * 100
+
+    100% = every test that reached validation was approved.
+    0%   = nothing has been validated yet.
+    Lower = backlog or quality issues blocking approvals.
+    """
+    validated_count = (
+        db.query(func.count(Test.id))
+        .filter(Test.state == TestState.validated)
+        .scalar()
+    ) or 0
+
+    rejected_count = (
+        db.query(func.count(Test.id))
+        .filter(Test.state == TestState.rejected)
+        .scalar()
+    ) or 0
+
+    in_review_count = (
+        db.query(func.count(Test.id))
+        .filter(Test.state == TestState.in_review)
+        .scalar()
+    ) or 0
+
+    total_in_pipeline = validated_count + rejected_count + in_review_count
+    conversion_rate = (
+        round(validated_count / total_in_pipeline * 100, 1)
+        if total_in_pipeline > 0
+        else 0.0
     )
 
-    if validated_weekly:
-        counts = [row.count for row in validated_weekly]
-        avg_per_week = round(sum(counts) / len(counts), 1)
-        recent = counts[-4:] if len(counts) >= 4 else counts
-        earlier = counts[-8:-4] if len(counts) >= 8 else counts[:len(counts) // 2] if counts else []
-
-        recent_avg = sum(recent) / len(recent) if recent else 0
-        earlier_avg = sum(earlier) / len(earlier) if earlier else 0
-
-        if recent_avg > earlier_avg * 1.1:
-            trend = "improving"
-        elif recent_avg < earlier_avg * 0.9:
-            trend = "declining"
-        else:
+    # Trend: compare conversion rate when considering pending tests
+    # High pending backlog relative to validated = declining
+    if total_in_pipeline == 0:
         trend = "stable"
+    elif in_review_count > validated_count:
+        trend = "declining"   # backlog building up
+    elif conversion_rate >= 80:
+        trend = "improving"   # most tests making it through
     else:
-        avg_per_week = 0
         trend = "stable"
 
     return {
-        "tests_per_week": avg_per_week,
+        "tests_per_week": conversion_rate,   # reuse key for API compat
+        "conversion_rate": conversion_rate,
+        "validated": validated_count,
+        "rejected": rejected_count,
+        "in_review": in_review_count,
         "trend": trend,
     }
 

frontend/src/pages/ExecutiveDashboardPage.tsx

@@ -498,11 +498,11 @@ export default function ExecutiveDashboardPage() {
           tooltip={{ description: "Percentage of executed attacks that Blue Team successfully detected. 100% means every simulated attack triggered an alert.", context: "Higher is better. Below 60% indicates significant detection gaps." }}
         />
         <KPICard
-          label="Validation Throughput"
-          value={opMetrics?.validation_throughput?.tests_per_week ?? 0}
-          unit="/week"
+          label="Pipeline Conversion"
+          value={opMetrics?.validation_throughput?.conversion_rate ?? opMetrics?.validation_throughput?.tests_per_week ?? 0}
+          unit="%"
           trend={opMetrics?.validation_throughput?.trend}
-          tooltip={{ description: "Number of tests fully validated (approved by Red + Blue leads) per week. Shows how productive the security testing programme is.", context: "Increasing trend = programme is accelerating. Decreasing may indicate process bottlenecks." }}
+          tooltip={{ description: "Percentage of tests that have entered the validation phase and been successfully approved. Formula: Validated ÷ (Validated + Rejected + In Review) × 100.", context: "100% = all reviewed tests approved. < 60% = quality or process issues. High backlog (many In Review) lowers this score." }}
         />
       </div>