ankn
a5f8fb49ab
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.
121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
"""
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SonarQube-Daten analysieren: Coverage und Smells pro Service/Team.
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Filtert auf master/main Branches und ordnet Services den Teams zu.
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"""
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import csv
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from collections import defaultdict
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CSV_PATH = "project-audit/data/sonarqube/sonarqube-metrics.csv"
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# Team-Zuordnung der Services
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TEAM_MAP = {
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"portal-ui": "404",
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"portal-middleware": "404",
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"portal-mw": "404",
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"steuerung-vertrieb": "CIB",
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"sv-": "CIB",
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"auftrags-verwaltung": "CIB",
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"av-": "CIB",
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"auftrag-service": "CIB",
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"archivierungsservice": "CIB",
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"common-interface": "Zero",
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"ci-": "Zero",
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"tadef": "Zero",
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"stammdaten": "Zero",
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"stb-": "Zero",
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"acat": "Zero",
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"vertragsdaten": "Zero",
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"vdv": "Zero",
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"ifp": "CIB",
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"kundendaten": "CIB",
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"kdv": "CIB",
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"rabatt": "CIB",
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"core-component": "Shared",
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"signature": "Shared",
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"logging": "Shared",
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}
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def get_team(name):
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name_lower = name.lower()
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for pattern, team in TEAM_MAP.items():
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if pattern in name_lower:
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return team
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return "Andere"
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# Daten laden (nur master/main)
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data = []
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with open(CSV_PATH, "r", encoding="utf-8") as f:
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reader = csv.DictReader(f, delimiter=";", quotechar='"')
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for row in reader:
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name = row.get("Name", "")
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if name.endswith("-master") or name.endswith("-main"):
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lines = int(row.get("Lines", "0") or "0")
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if lines > 0: # Nur Projekte mit Code
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data.append({
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"name": name.replace("-master", "").replace("-main", ""),
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"lines": lines,
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"bugs": int(row.get("Bugs", "0") or "0"),
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"smells": int(row.get("CodeSmells", "0") or "0"),
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"coverage": float(row.get("Coverage", "0") or "0"),
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"duplications": float(row.get("Duplications", "0") or "0"),
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"qgate": row.get("QualityGate", "?"),
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"team": get_team(name),
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})
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# Sortiert nach Lines
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data.sort(key=lambda x: x["lines"], reverse=True)
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print(f"=== SonarQube Analyse (nur master/main, {len(data)} Projekte mit Code) ===")
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print()
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# Top 20 nach Groesse
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print("--- Top 20 Projekte nach Groesse ---")
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print(f"{'Projekt':<40} {'Lines':>7} {'Bugs':>5} {'Smells':>7} {'Cov%':>6} {'QGate':>6} {'Team':<8}")
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print("-" * 85)
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for row in data[:20]:
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cov_str = f"{row['coverage']:.1f}" if row['coverage'] > 0 else "-"
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print(f"{row['name']:<40} {row['lines']:>7} {row['bugs']:>5} {row['smells']:>7} {cov_str:>6} {row['qgate']:>6} {row['team']:<8}")
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# Team-Aggregation
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print()
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print("--- Aggregation pro Team ---")
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teams = defaultdict(lambda: {"lines": 0, "bugs": 0, "smells": 0, "cov_weighted": 0, "projects": 0, "qgate_ok": 0, "qgate_err": 0})
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for row in data:
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t = teams[row["team"]]
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t["lines"] += row["lines"]
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t["bugs"] += row["bugs"]
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t["smells"] += row["smells"]
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t["cov_weighted"] += row["coverage"] * row["lines"]
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t["projects"] += 1
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if row["qgate"] == "OK":
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t["qgate_ok"] += 1
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else:
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t["qgate_err"] += 1
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print(f"{'Team':<10} {'Projekte':>8} {'Lines':>8} {'Bugs':>5} {'Smells':>7} {'Smells/1K':>9} {'Cov%':>6} {'QGate OK':>8}")
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print("-" * 75)
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for team in ["404", "CIB", "Zero", "Shared", "Andere"]:
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if team in teams:
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t = teams[team]
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smells_per_k = (t["smells"] / t["lines"] * 1000) if t["lines"] > 0 else 0
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avg_cov = (t["cov_weighted"] / t["lines"]) if t["lines"] > 0 else 0
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qgate_str = f"{t['qgate_ok']}/{t['projects']}"
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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}")
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# Projekte ohne Coverage
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print()
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print("--- Projekte OHNE Coverage (0% oder keine Daten) ---")
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no_cov = [r for r in data if r["coverage"] == 0 and r["lines"] > 500]
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no_cov.sort(key=lambda x: x["lines"], reverse=True)
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for row in no_cov:
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print(f" {row['name']:<40} {row['lines']:>7} Lines {row['team']:<8}")
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# Quality Gate Failures
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print()
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print("--- Quality Gate FAILED (ERROR) ---")
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failed = [r for r in data if r["qgate"] == "ERROR"]
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failed.sort(key=lambda x: x["lines"], reverse=True)
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for row in failed[:15]:
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cov_str = f"{row['coverage']:.1f}%" if row['coverage'] > 0 else "0%"
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print(f" {row['name']:<40} {row['lines']:>7} Lines Bugs={row['bugs']} Smells={row['smells']} Cov={cov_str} {row['team']}")
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