Feature: Detaillierte Worker-Pool Performance-Metriken mit psutil

Neue Metrik-Erfassung für Saxon- und FOP-Worker-Pools:
- Kompilierungszeit der Java-Worker-Klassen
- Worker-Startzeiten (Summe + Durchschnitt pro Worker)
- RAM-Verbrauch vor/nach Transformation (Summe + Durchschnitt)
- Automatische Berechnung der RAM-Zunahme in MB und Prozent

Technische Details:
- Neue WorkerPoolMetrics-Datenklasse in worker_metrics.py
- RAM-Messung via psutil (v7.2.1, neu hinzugefügt)
- Metriken für beide Saxon-Varianten (JAXP + s9api)
- WorkerPoolMetricsDialog mit Tab-basierter UI
- Menüeintrag "Projekt → Worker-Pool-Metriken"

Metriken werden automatisch erfasst:
- Bei Worker-Pool-Initialisierung (Kompilierung + Start)
- Vor erster Transformation (RAM-Baseline)
- Nach allen Transformationen (RAM-Endwert)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

src/worker_metrics.py

@@ -0,0 +1,62 @@
+"""
+Worker Pool Performance-Metriken.
+
+Gemeinsame Datenstrukturen für Performance- und Ressourcenverbrauch-Metriken
+der Worker-Pools (Saxon, FOP).
+"""
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class WorkerPoolMetrics:
+    """
+    Metriken für Worker-Pool Performance und Ressourcenverbrauch.
+    """
+
+    # Kompilierungszeit
+    compilation_time_seconds: float = 0.0
+
+    # Worker-Start-Zeiten
+    worker_start_times: list[float] = field(default_factory=list)
+    total_worker_start_time_seconds: float = 0.0
+    average_worker_start_time_seconds: float = 0.0
+
+    # RAM-Verbrauch (in MB)
+    ram_before_transform_mb_per_worker: list[float] = field(default_factory=list)
+    ram_after_transform_mb_per_worker: list[float] = field(default_factory=list)
+    total_ram_before_mb: float = 0.0
+    total_ram_after_mb: float = 0.0
+    average_ram_before_mb: float = 0.0
+    average_ram_after_mb: float = 0.0
+
+    # XSL-Kompilierungszeiten (nur für Saxon)
+    xsl_compilation_times: list[float] = field(default_factory=list)
+    total_xsl_compilation_time_seconds: float = 0.0
+    average_xsl_compilation_time_seconds: float = 0.0
+
+    def calculate_aggregates(self):
+        """Berechnet aggregierte Werte (Summen, Durchschnitte)."""
+        # Worker-Start-Zeiten
+        if self.worker_start_times:
+            self.total_worker_start_time_seconds = sum(self.worker_start_times)
+            self.average_worker_start_time_seconds = self.total_worker_start_time_seconds / len(
+                self.worker_start_times
+            )
+
+        # RAM vor Transformation
+        if self.ram_before_transform_mb_per_worker:
+            self.total_ram_before_mb = sum(self.ram_before_transform_mb_per_worker)
+            self.average_ram_before_mb = self.total_ram_before_mb / len(self.ram_before_transform_mb_per_worker)
+
+        # RAM nach Transformation
+        if self.ram_after_transform_mb_per_worker:
+            self.total_ram_after_mb = sum(self.ram_after_transform_mb_per_worker)
+            self.average_ram_after_mb = self.total_ram_after_mb / len(self.ram_after_transform_mb_per_worker)
+
+        # XSL-Kompilierungszeiten
+        if self.xsl_compilation_times:
+            self.total_xsl_compilation_time_seconds = sum(self.xsl_compilation_times)
+            self.average_xsl_compilation_time_seconds = self.total_xsl_compilation_time_seconds / len(
+                self.xsl_compilation_times
+            )