Metrik & Pengukuran Keandalan Perangkat Lunak

# ================================================================
# S11409 - Sesi 2: Menghitung Metrik Keandalan Software
# Dosen: Riadi Marta Dinata, S.Ti., M.Kom. | ISTN Jakarta
# ================================================================

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# ── DATA: Riwayat Kegagalan Sistem (jam operasi antar failure) ──
data_kegagalan = {
    'failure_ke': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'waktu_operasi': [142, 98, 167, 203, 88, 175, 220, 145, 190, 160],  # jam
    'waktu_repair': [2, 3, 1, 4, 2, 1, 5, 2, 3, 2]           # jam
}
df = pd.DataFrame(data_kegagalan)

# ── MENGHITUNG METRIK ──
total_operasi = df['waktu_operasi'].sum()
total_repair  = df['waktu_repair'].sum()
jumlah_failure = len(df)

mttf        = total_operasi / jumlah_failure
mttr        = total_repair  / jumlah_failure
mtbf        = mttf + mttr
failure_rate = 1 / mttf
availability = mttf / mtbf * 100

# ── TAMPILKAN HASIL ──
print("="*50)
print("  LAPORAN METRIK KEANDALAN SISTEM")
print("="*50)
print(f"Total waktu operasi  : {total_operasi} jam")
print(f"Total waktu perbaikan: {total_repair} jam")
print(f"Jumlah kegagalan     : {jumlah_failure} kali")
print("-"*50)
print(f"MTTF (jam/failure)   : {mttf:.2f} jam")
print(f"MTTR (jam/perbaikan) : {mttr:.2f} jam")
print(f"MTBF (jam)           : {mtbf:.2f} jam")
print(f"Failure Rate (λ)     : {failure_rate:.5f} per jam")
print(f"Availability         : {availability:.4f}%")

# ── VISUALISASI ──
fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Plot 1: Waktu operasi per failure
axes[0].bar(df['failure_ke'], df['waktu_operasi'],
            color='#C8102E', alpha=0.8, label='Waktu Operasi')
axes[0].axhline(y=mttf, color='#D4A017', linestyle='--',
                 linewidth=2, label=f'MTTF = {mttf:.1f} jam')
axes[0].set_xlabel('Ke-n Kegagalan')
axes[0].set_ylabel('Jam Operasi')
axes[0].set_title('Waktu Operasi Antar Kegagalan')
axes[0].legend()

# Plot 2: Perbandingan MTTF, MTTR, MTBF
labels  = ['MTTF', 'MTTR', 'MTBF']
values  = [mttf, mttr, mtbf]
colors  = ['#C8102E', '#22C55E', '#38BDF8']
axes[1].bar(labels, values, color=colors, alpha=0.85)
axes[1].set_ylabel('Jam')
axes[1].set_title('Perbandingan Metrik Keandalan')
for i, v in enumerate(values):
    axes[1].text(i, v+1, f'{v:.1f}', ha='center', fontweight='bold')

plt.tight_layout()
plt.savefig('metrik_keandalan.png', dpi=150, bbox_inches='tight')
print("\nGrafik disimpan: metrik_keandalan.png")

# ── MENGHITUNG DRE ──
defect_pre_rilis  = 85   # ditemukan saat QA
defect_post_rilis = 15   # ditemukan pengguna setelah rilis
DRE = defect_pre_rilis / (defect_pre_rilis + defect_post_rilis) * 100
print(f"\nDefect Removal Efficiency (DRE): {DRE:.1f}%")
if DRE >= 95:
    print("  ✅ SANGAT BAIK - Proses QA sudah sangat efektif!")
elif DRE >= 85:
    print("  ⚠️  CUKUP BAIK - Masih perlu ditingkatkan")
else:
    print("  ❌ PERLU PERBAIKAN - Banyak defect lolos ke produksi!")