Lab 08: Quality Metrics & Statistical Quality Control¶

import json
import statistics

# ── Cyclomatic Complexity ──────────────────────────────────────────
with open("cc_metrics.json") as f:
    cc_data = json.load(f)

all_functions = []
for filepath, blocks in cc_data.items():
    for block in blocks:
        all_functions.append({
            "file": filepath,
            "name": block["name"],
            "complexity": block["complexity"],
            "rank": block["rank"]
        })

complexities = [f["complexity"] for f in all_functions]
print("=== Cyclomatic Complexity ===")
print(f"Total functions analyzed: {len(all_functions)}")
print(f"Average complexity: {statistics.mean(complexities):.2f}")
print(f"Median complexity: {statistics.median(complexities):.2f}")
print(f"Max complexity: {max(complexities)} (threshold: 10 = HIGH RISK)")
print(f"Standard deviation: {statistics.stdev(complexities):.2f}")

# Risk distribution
rank_counts = {}
for f in all_functions:
    rank_counts[f["rank"]] = rank_counts.get(f["rank"], 0) + 1
print("\nRisk Distribution:")
for rank in sorted(rank_counts):
    label = {"A":"Low","B":"Low","C":"Medium","D":"High","E":"Very High","F":"Extreme"}.get(rank,"?")
    print(f"  Rank {rank} ({label}): {rank_counts[rank]} functions")

# Top 10 most complex
print("\nTop 10 Most Complex Functions:")
for f in sorted(all_functions, key=lambda x: x["complexity"], reverse=True)[:10]:
    print(f"  CC={f['complexity']:3d} [{f['rank']}]  {f['name']}  ({f['file']})")

# ── Maintainability Index ──────────────────────────────────────────
with open("mi_metrics.json") as f:
    mi_data = json.load(f)

mi_scores = [v["mi"] for v in mi_data.values() if isinstance(v, dict) and "mi" in v]
print(f"\n=== Maintainability Index ===")
print(f"Average MI: {statistics.mean(mi_scores):.1f} (threshold: <65 = unmaintainable)")
low_mi = [k for k, v in mi_data.items() if isinstance(v, dict) and v.get("mi", 100) < 65]
print(f"Files below MI threshold: {len(low_mi)}")
for f in low_mi:
    print(f"  {f}: MI={mi_data[f]['mi']:.1f}")

# sprint_defects.py — simulated defect data for a 3-year-old project
import json

# Format: {sprint: {defects_found, kloc_changed, defect_density}}
# Defect density = defects per KLOC (thousand lines of code changed)
sprint_data = [
    {"sprint": 1,  "defects": 12, "kloc": 3.2, "density": 3.75},
    {"sprint": 2,  "defects": 8,  "kloc": 2.8, "density": 2.86},
    {"sprint": 3,  "defects": 15, "kloc": 4.1, "density": 3.66},
    {"sprint": 4,  "defects": 6,  "kloc": 2.1, "density": 2.86},
    {"sprint": 5,  "defects": 9,  "kloc": 3.0, "density": 3.00},
    {"sprint": 6,  "defects": 22, "kloc": 4.5, "density": 4.89},  # spike
    {"sprint": 7,  "defects": 11, "kloc": 3.3, "density": 3.33},
    {"sprint": 8,  "defects": 7,  "kloc": 2.5, "density": 2.80},
    {"sprint": 9,  "defects": 5,  "kloc": 2.2, "density": 2.27},
    {"sprint": 10, "defects": 4,  "kloc": 2.0, "density": 2.00},
    {"sprint": 11, "defects": 18, "kloc": 3.8, "density": 4.74},  # second spike
    {"sprint": 12, "defects": 6,  "kloc": 2.3, "density": 2.61},
]

import matplotlib.pyplot as plt
import numpy as np

densities = [d["density"] for d in sprint_data]
sprints = [d["sprint"] for d in sprint_data]

mean = np.mean(densities)
std = np.std(densities, ddof=1)

ucl = mean + 3 * std   # Upper Control Limit
lcl = max(0, mean - 3 * std)  # Lower Control Limit (floored at 0)

fig, ax = plt.subplots(figsize=(12, 6))

ax.plot(sprints, densities, 'o-', color='steelblue', linewidth=2, label='Defect Density')
ax.axhline(mean, color='green', linestyle='--', linewidth=1.5, label=f'Mean = {mean:.2f}')
ax.axhline(ucl, color='red', linestyle='--', linewidth=1.5, label=f'UCL = {ucl:.2f}')
ax.axhline(lcl, color='orange', linestyle='--', linewidth=1.5, label=f'LCL = {lcl:.2f}')

# Highlight out-of-control points
for i, (s, d) in enumerate(zip(sprints, densities)):
    if d > ucl or d < lcl:
        ax.plot(s, d, 'r*', markersize=15, label='Out of Control' if i == 0 else "")

ax.set_xlabel("Sprint Number")
ax.set_ylabel("Defect Density (defects/KLOC)")
ax.set_title("Defect Density Control Chart — SCIA-425 Lab 08")
ax.legend()
ax.grid(True, alpha=0.3)
ax.set_xticks(sprints)

plt.tight_layout()
plt.savefig("control_chart.png", dpi=150)
print(f"Control chart saved. Mean={mean:.2f}, UCL={ucl:.2f}, LCL={lcl:.2f}")
print(f"Out-of-control points: {[s for s,d in zip(sprints,densities) if d > ucl or d < lcl]}")