Migrate all repos into monorepo context folders

Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp, Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit, Projekt-KIQ-HP, teamlandkarte-mcp Dhive: Jury-Voting Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation) Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more Shared/references: symphony (read-only) Bahn repos remain available as independent remotes - this monorepo pulls them in via subtree, the originals are untouched.
2026-06-30 20:39:52 +02:00
parent 2f2b295531
commit a5f8fb49ab
1717 changed files with 447332 additions and 0 deletions
@@ -0,0 +1,120 @@
+"""
+SonarQube-Daten analysieren: Coverage und Smells pro Service/Team.
+Filtert auf master/main Branches und ordnet Services den Teams zu.
+"""
+import csv
+from collections import defaultdict
+
+CSV_PATH = "project-audit/data/sonarqube/sonarqube-metrics.csv"
+
+# Team-Zuordnung der Services
+TEAM_MAP = {
+    "portal-ui": "404",
+    "portal-middleware": "404",
+    "portal-mw": "404",
+    "steuerung-vertrieb": "CIB",
+    "sv-": "CIB",
+    "auftrags-verwaltung": "CIB",
+    "av-": "CIB",
+    "auftrag-service": "CIB",
+    "archivierungsservice": "CIB",
+    "common-interface": "Zero",
+    "ci-": "Zero",
+    "tadef": "Zero",
+    "stammdaten": "Zero",
+    "stb-": "Zero",
+    "acat": "Zero",
+    "vertragsdaten": "Zero",
+    "vdv": "Zero",
+    "ifp": "CIB",
+    "kundendaten": "CIB",
+    "kdv": "CIB",
+    "rabatt": "CIB",
+    "core-component": "Shared",
+    "signature": "Shared",
+    "logging": "Shared",
+}
+
+def get_team(name):
+    name_lower = name.lower()
+    for pattern, team in TEAM_MAP.items():
+        if pattern in name_lower:
+            return team
+    return "Andere"
+
+# Daten laden (nur master/main)
+data = []
+with open(CSV_PATH, "r", encoding="utf-8") as f:
+    reader = csv.DictReader(f, delimiter=";", quotechar='"')
+    for row in reader:
+        name = row.get("Name", "")
+        if name.endswith("-master") or name.endswith("-main"):
+            lines = int(row.get("Lines", "0") or "0")
+            if lines > 0:  # Nur Projekte mit Code
+                data.append({
+                    "name": name.replace("-master", "").replace("-main", ""),
+                    "lines": lines,
+                    "bugs": int(row.get("Bugs", "0") or "0"),
+                    "smells": int(row.get("CodeSmells", "0") or "0"),
+                    "coverage": float(row.get("Coverage", "0") or "0"),
+                    "duplications": float(row.get("Duplications", "0") or "0"),
+                    "qgate": row.get("QualityGate", "?"),
+                    "team": get_team(name),
+                })
+
+# Sortiert nach Lines
+data.sort(key=lambda x: x["lines"], reverse=True)
+
+print(f"=== SonarQube Analyse (nur master/main, {len(data)} Projekte mit Code) ===")
+print()
+
+# Top 20 nach Groesse
+print("--- Top 20 Projekte nach Groesse ---")
+print(f"{'Projekt':<40} {'Lines':>7} {'Bugs':>5} {'Smells':>7} {'Cov%':>6} {'QGate':>6} {'Team':<8}")
+print("-" * 85)
+for row in data[:20]:
+    cov_str = f"{row['coverage']:.1f}" if row['coverage'] > 0 else "-"
+    print(f"{row['name']:<40} {row['lines']:>7} {row['bugs']:>5} {row['smells']:>7} {cov_str:>6} {row['qgate']:>6} {row['team']:<8}")
+
+# Team-Aggregation
+print()
+print("--- Aggregation pro Team ---")
+teams = defaultdict(lambda: {"lines": 0, "bugs": 0, "smells": 0, "cov_weighted": 0, "projects": 0, "qgate_ok": 0, "qgate_err": 0})
+for row in data:
+    t = teams[row["team"]]
+    t["lines"] += row["lines"]
+    t["bugs"] += row["bugs"]
+    t["smells"] += row["smells"]
+    t["cov_weighted"] += row["coverage"] * row["lines"]
+    t["projects"] += 1
+    if row["qgate"] == "OK":
+        t["qgate_ok"] += 1
+    else:
+        t["qgate_err"] += 1
+
+print(f"{'Team':<10} {'Projekte':>8} {'Lines':>8} {'Bugs':>5} {'Smells':>7} {'Smells/1K':>9} {'Cov%':>6} {'QGate OK':>8}")
+print("-" * 75)
+for team in ["404", "CIB", "Zero", "Shared", "Andere"]:
+    if team in teams:
+        t = teams[team]
+        smells_per_k = (t["smells"] / t["lines"] * 1000) if t["lines"] > 0 else 0
+        avg_cov = (t["cov_weighted"] / t["lines"]) if t["lines"] > 0 else 0
+        qgate_str = f"{t['qgate_ok']}/{t['projects']}"
+        print(f"{team:<10} {t['projects']:>8} {t['lines']:>8} {t['bugs']:>5} {t['smells']:>7} {smells_per_k:>9.1f} {avg_cov:>6.1f} {qgate_str:>8}")
+
+# Projekte ohne Coverage
+print()
+print("--- Projekte OHNE Coverage (0% oder keine Daten) ---")
+no_cov = [r for r in data if r["coverage"] == 0 and r["lines"] > 500]
+no_cov.sort(key=lambda x: x["lines"], reverse=True)
+for row in no_cov:
+    print(f"  {row['name']:<40} {row['lines']:>7} Lines  {row['team']:<8}")
+
+# Quality Gate Failures
+print()
+print("--- Quality Gate FAILED (ERROR) ---")
+failed = [r for r in data if r["qgate"] == "ERROR"]
+failed.sort(key=lambda x: x["lines"], reverse=True)
+for row in failed[:15]:
+    cov_str = f"{row['coverage']:.1f}%" if row['coverage'] > 0 else "0%"
+    print(f"  {row['name']:<40} {row['lines']:>7} Lines  Bugs={row['bugs']} Smells={row['smells']} Cov={cov_str}  {row['team']}")