I'm considering how to measure software quality in automobiles. I'm thinking of selecting around 30 indicators.
vocational training center as a software factory. (3) Software Quality in use add something.
https://qiita.com/kaizen_nagoya/items/0492b305c42e09c77fa8
ChatGPT
How would you measure the software quality of the following products? Please explain and list three metrics for each product. 1. mobility(people in it), 2.machine tools(plant), 3 Power supply, 4 game or mobile phone, 5 home appliances
ChatGPT:
Here’s a way to think about how to measure software quality for each type of product and a set of three concrete, commonly used metrics you could track for each.
The key is that “quality” depends on what the software must achieve in that domain (safety, reliability, responsiveness, etc.).
- Mobility (e.g., cars, buses, trains carrying people)
These systems are safety-critical and operate in real time.
Focus: Safety, reliability, and real-time performance.
Example Metrics
Mean Time Between Failures (MTBF): Hours or kilometers of operation per software failure.
Safety-Critical Defect Density: Number of high-severity safety defects per 1,000 source lines of code (KSLOC).
Real-Time Response Latency: Worst-case time to respond to control inputs (e.g., braking signal), measured in milliseconds.- Machine Tools (Industrial Plant Equipment)
CNC machines, robotic arms, etc., where downtime is costly.
Focus: Precision, uptime, maintainability.
Example Metrics
Overall Equipment Effectiveness (OEE) Impact: Percentage of machine availability lost due to software faults.
Process Accuracy/Error Rate: Number of production errors per thousand operations caused by software.
Mean Time to Recovery (MTTR): Average time to restore normal operation after a software-related fault.- Power Supply (e.g., smart grids, UPS control software)
Must run continuously and manage high loads safely.
Focus: Continuous availability, fault tolerance.
Example Metrics
System Availability (% Uptime): (Total time – downtime)/total time × 100.
Fault Detection Latency: Average time to detect and log a grid or supply anomaly.
Compliance Rate to Regulatory Standards: Percentage of test cases meeting power-industry safety/quality standards (e.g., IEC 61508).- Game or Mobile Phone Applications
User-facing, highly interactive software.
Focus: Performance, usability, user satisfaction.
Example Metrics
Crash-Free Sessions: Percentage of sessions that end without a crash.
Average Frame Rate / App Responsiveness: Frames per second or average UI response time.
User Retention or App Store Rating: e.g., 30-day retention rate or average star rating as a proxy for perceived quality.- Home Appliances (e.g., smart refrigerator, washing machine)
Embedded systems often used by non-technical users.
Focus: Reliability, energy efficiency, ease of update.
Example Metrics
Field Failure Rate: Failures per thousand units per month.
Firmware Update Success Rate: Percentage of over-the-air updates completed without user intervention or rollback.
Power Efficiency Under Software Control: Measured energy consumption compared to design target (kWh per cycle).
✅How to Use These Metrics
Collect data from logs, automated tests, and field reports.
Trend analysis over time (e.g., quarterly MTBF).
Benchmark against industry standards or internal targets to decide whether quality is improving.
These metrics are illustrative; in practice, teams tailor them to the product’s risk level, regulatory requirements, and customer expectations.
I understand that three measurements are important for Mobility. Mobility software i s very large, so please list 32 i measurement methods that can be measured in the intermediate products of creating the software.
ChatGPT:
Below is a broad set of 32 measurement methods that automotive/rail/other mobility software teams often use during development (not just after release) to monitor and improve quality.
I grouped them roughly by development stage so you can pick and choose.
1️⃣ Requirements & Architecture (up-front quality)
Requirements Volatility – % of requirements changed after baseline.
Requirements Coverage – % of requirements with at least one test case.
Safety Goal Traceability – Links from hazards to software components.
Architecture Complexity (Cyclomatic at component level) – Early static analysis of proposed modules.
Interface Count / Coupling Index – Number of external interfaces per component.
2️⃣ Design & Static Analysis
Code Complexity (Cyclomatic / Cognitive) – Function-level metrics.
Code Churn – Lines added/removed per iteration.
Comment Density / Self-Documentation Score.
Dead Code Ratio – % of unreachable or unused functions.
MISRA / AUTOSAR Coding Rule Violations.
Static Security Findings (e.g., buffer overflow risk count).
Data & Control Coupling – Fan-in / fan-out measures.
Memory Footprint Growth vs. Budget.
Stack-Usage Estimation per Task.
3️⃣ Build & Integration
Build Failure Rate – % of CI builds failing.
Integration Defect Density – Defects found during subsystem integration per KLOC.
Automated Build Time Trend – Detecting growing build complexity.
Dependency Graph Size – Third-party library count/versions.
4️⃣ Unit & Module Testing
Unit Test Code Coverage (statement/branch/MCDC).
Mutation Testing Score – % of injected faults caught by tests.
Defect Discovery Rate – Defects per staff-hour in unit testing.
Test Case Effectiveness – % of tests finding at least one defect.
Static Timing Analysis vs. Real-Time Deadlines.
5️⃣ System / Hardware-in-the-Loop (HIL)
HIL Scenario Coverage – % of operational scenarios exercised.
Worst-Case Execution Time (WCET) Margin.
Fault-Injection Recovery Rate – % of injected faults safely handled.
CPU Utilization Headroom – Peak % of CPU usage under stress.
Memory-Leak Detection Count.
6️⃣ Reliability & Safety Verification
Mean Time Between Detected Faults (pre-release testing).
Safety Requirement Test Pass Rate (ISO 26262).
Failure Mode Coverage – % of identified FMEA modes tested.
Diagnostic Coverage – % of possible hardware/software faults detected and reported.
How to Apply
Automate collection in CI/CD (e.g., static analysis, coverage).
Dashboard trends per sprint to catch regressions early.
Tie key metrics (like WCET margin or diagnostic coverage) to release gates to ensure safety and performance before mass production.
These intermediate measurements give a detailed, continuous picture of mobility-software quality long before the vehicle is on the road.
Please provide specific methods, tools, and source code for measuring items 11 to 15.
ChatGPT
回答 1
Here are specific measurement approaches, recommended tools, and sample code snippets for items 11–15 in the intermediate-product mobility-software quality list.
11️⃣ Requirements Traceability Coverage
Goal: Every requirement is linked to design, code, and tests.
Methods & Tools
ReqView, Jama Connect, Polarion, DOORS Next – manage requirements and links.
For open-source + Git:
– Store requirements as YAML/Markdown with unique IDs.
– Cross-check IDs in code/tests.
Example (Python checker)
# requirements.yml
- REQ-001: braking distance calculation
- REQ-002: ABS fault logging
# traceability_check.py
import re, yaml, pathlib
reqs = [r['REQ'] for r in yaml.safe_load(open('requirements.yml'))]
found = set()
for p in pathlib.Path('src').rglob('*.c'):
text = p.read_text()
found |= {m.group(0) for m in re.finditer(r'REQ-\d+', text)}
missing = set(reqs) - found
print("Missing links:", missing)
12️⃣ Architectural Layer Coupling
Goal: Detect unwanted dependencies between layers (e.g., UI ↔ hardware).
Methods & Tools
SonarQube “Architecture” / “Tangle” metrics
ArchUnit (Java), Import Linter (Python)
Graph-based analysis with networkx
Example (Python with Import-Linter)
pip install import-linter
importlinter.ini
[importlinter:contract:layers]
name = Clean layering
layers = ui,service,infra
allow = ui->service, service->infra
lint-imports
Fails if, e.g., UI imports infra directly.
13️⃣ Build Reproducibility
Goal: Same source + environment → identical binary.
Methods & Tools
reprotest (Debian), bazel --hash, Nix/Guix.
Hash build artifacts and compare.
Example (simple script)
make clean && make
sha256sum build/firmware.bin > hash1.txt
touch src/dummy_comment.c # innocuous change
make clean && make
sha256sum build/firmware.bin > hash2.txt
diff hash1.txt hash2.txt || echo "Not reproducible!"
14️⃣ Concurrency / Thread Safety Analysis
Goal: Find race conditions and deadlocks early.
Methods & Tools
ThreadSanitizer (-fsanitize=thread with Clang/GCC)
Helgrind (Valgrind)
Static: Coverity Concurrency Checker
Example (C/C++)
clang++ -fsanitize=thread -g -O1 -o app app.cpp
./app # runtime races reported
For Python asyncio race detection: pytest-asyncio + custom checks.
15️⃣ Logging & Diagnostics Quality
Goal: Ensure critical events are logged with adequate detail and without PII leakage.
Methods & Tools
loglint, custom regex scanning.
Measure log coverage in tests with pytest or unittest.
Example (Python)
# log_check.py
import re, pathlib
critical = ["CRASH", "BRAKE_FAIL", "AIRBAG"]
missing = []
for p in pathlib.Path("src").rglob("*.py"):
txt = p.read_text()
for c in critical:
if c not in txt:
missing.append((p, c))
print("Missing critical logs:", missing)
Or validate runtime logging:
import logging, io
buf = io.StringIO()
logging.basicConfig(stream=buf, level=logging.INFO)
# run tests...
assert "BRAKE_FAIL" in buf.getvalue()
Integration Tips
Automate these checks in CI (GitHub Actions, GitLab CI, Jenkins).
Gate merges if thresholds are not met (e.g., 100 % requirement link coverage, zero unexpected layer imports).
Dashboard results using SonarQube or Grafana for continuous visibility.
These methods let you quantitatively assess requirements coverage, architectural discipline, deterministic builds, concurrency safety, and diagnostic robustness—key quality dimensions for large mobility-software projects.
ChatGPT
回答 2
Great 👍 let’s continue with items 11–15 from the intermediate software quality metrics list for mobility software.
I’ll provide methods, tools, and example source code for each.
1️⃣1️⃣ Code Duplication (Clones)
Purpose: Detect copy-paste code that increases maintenance cost and bug risk.
Methods & Tools
SonarQube → “Duplications” metric
PMD CPD (Copy/Paste Detector) (multi-language)
jscpd (Node.js, supports 150+ languages)
Example (jscpd)
npm install -g jscpd
jscpd --reporters json --output reports/duplication src/
# parse_duplication.py
import json
data = json.load(open("reports/duplication/jscpd-report.json"))
print("Duplicated lines:", data["statistics"]["total"]["duplicatedLines"])
print("Duplication %:", data["statistics"]["total"]["percentage"])
1️⃣2️⃣ Test Coverage (Statement / Branch / MC/DC)
Purpose: Verify tests adequately exercise safety-critical code.
Methods & Tools
gcov/lcov for C/C++ (statement/branch coverage)
JaCoCo for Java (branch/class/method coverage)
pytest-cov for Python
VectorCAST / Tessy (commercial, MC/DC coverage for ISO 26262)
Example (C/C++ with lcov)
# compile with coverage
g++ -fprofile-arcs -ftest-coverage main.cpp -o app
./app
lcov --capture --directory . --output-file coverage.info
genhtml coverage.info --output-directory out
Parse coverage
import re
text = open("coverage.info").read()
covered = sum(int(m.group(1)) for m in re.finditer(r"LF:(\d+)", text))
executed = sum(int(m.group(1)) for m in re.finditer(r"LH:(\d+)", text))
print("Coverage %:", executed / covered * 100)
1️⃣3️⃣ Requirements Traceability Coverage
Purpose: Ensure each requirement has corresponding implementation & test.
Methods & Tools
ReqIF + DOORS / Polarion / Jama (enterprise)
Open-source: ReqIF Studio
Lightweight → maintain requirements.csv with mappings.
Example (simple Python checker)
# requirements.csv
REQ-001, implemented_in=src/brake.cpp, tested_in=tests/brake_test.cpp
REQ-002, implemented_in=src/steering.cpp, tested_in=tests/steering_test.cpp
import csv, os
with open("requirements.csv") as f:
reader = csv.reader(f)
for row in reader:
req, impl, test = row
impl_file = impl.split("=")[1]
test_file = test.split("=")[1]
print(req,
"impl OK" if os.path.exists(impl_file) else "MISSING impl",
"test OK" if os.path.exists(test_file) else "MISSING test")
1️⃣4️⃣ Static Analysis Warnings
Purpose: Detect bugs and vulnerabilities before runtime.
Methods & Tools
clang-tidy, cppcheck, pylint, FindBugs/SpotBugs (Java)
SonarQube aggregates results across languages.
Example (clang-tidy)
clang-tidy src/*.cpp -- -std=c++17 > tidy_report.txt
# count_warnings.py
text = open("tidy_report.txt").read().splitlines()
warns = sum(1 for l in text if "warning:" in l)
errs = sum(1 for l in text if "error:" in l)
print("Static warnings:", warns, "errors:", errs)
1️⃣5️⃣ Coding Style Consistency
Purpose: Enforce a uniform code style (readability, fewer merge conflicts).
Methods & Tools
clang-format (C/C++/Java/JavaScript)
Black (Python)
Prettier (JS/TS/HTML)
Example (clang-format check)
clang-format -style=file --dry-run --Werror src/*.cpp
Python auto-check (Black)
pip install black
black --check src/
# parse_black.py
import subprocess
res = subprocess.run(["black", "--check", "src/"], capture_output=True, text=True)
print("Style issues:", "no issues" if res.returncode == 0 else res.stdout)
✅ Summary (11–15)
Code duplication → jscpd, SonarQube
Test coverage → lcov, JaCoCo, pytest-cov, VectorCAST
Requirements traceability → DOORS/ReqIF or simple CSV + script
Static analysis warnings → clang-tidy, cppcheck, SonarQube
Coding style → clang-format, Black, Prettier
All integrate easily into CI/CD to track intermediate software quality before final release.
👉 Would you like me to continue with items 16–20 (Memory leaks, Thread safety, Performance regression, etc.) in the same detailed format with tools + code examples?
この回答がよい
Please provide specific methods, tools, and source code for measuring items 16 to 20.
ChatGPT:
Here are practical methods, recommended tools, and sample source-code snippets for measuring items 16–20 of the mobility-software intermediate-quality metrics list.
16️⃣ Memory Usage / Leak Detection
Purpose: Find leaks and excessive allocations early.
Language Tools Notes
C/C++ Valgrind / Memcheck, AddressSanitizer (ASan) Detect leaks, invalid reads/writes
Java VisualVM, Eclipse MAT Heap dumps
Python tracemalloc Allocation tracing
Example (C++ with Valgrind)
valgrind --leak-check=full ./app
Output shows bytes lost and call stacks.
Python tracking
import tracemalloc
tracemalloc.start()
# ... run code ...
current, peak = tracemalloc.get_traced_memory()
print(f"Current: {current/1024:.1f} KB Peak: {peak/1024:.1f} KB")
17️⃣ Concurrency / Thread-Safety Issues
Purpose: Detect race conditions, deadlocks.
Language Tools Features
C/C++ ThreadSanitizer (TSan), clang/gcc Data race detection
Java FindBugs/SpotBugs “MT” rules, Java Concurrency Stress tests
Python faulthandler, custom stress tests GIL hides many races, but I/O races possible
Example (C++ TSan)
clang++ -fsanitize=thread -g -O1 -o app main.cpp
TSAN_OPTIONS="halt_on_error=1" ./app
Python race stress test (basic)
import threading
counter = 0
def task():
global counter
for _ in range(10_000):
counter += 1
threads = [threading.Thread(target=task) for _ in range(8)]
[t.start() for t in threads]
[t.join() for t in threads]
print("Counter =", counter)
If counter < 80 000, you have a race (simulate with ctypes atomic ops to fix).
18️⃣ CPU / Performance Regression
Purpose: Monitor runtime speed across commits.
Tool Language Feature
perf (Linux) C/C++/Rust CPU events, cycles
gprof, callgrind C/C++ Profiling
pytest-benchmark, timeit Python Unit performance
Benchmark.js JavaScript Micro-benchmarks
Example (Linux perf)
perf stat ./app
Python benchmark
import timeit
print(timeit.timeit("process()", setup="from mymod import process", number=100))
Integrate into CI with thresholds, e.g., “benchmark X must run < 200 ms”.
19️⃣ Security Vulnerabilities (Static + Dependency)
Purpose: Identify common vulnerabilities (buffer overflow, outdated libs).
Area Tools
Code clang-tidy security checks, Bandit (Python), SonarQube Security Hotspots
Dependencies OWASP Dependency-Check, pip-audit, npm audit
Examples
# Python code scan
bandit -r src/ > bandit_report.txt
# Dependency check
pip install pip-audit
pip-audit
Parse Bandit report:
with open("bandit_report.txt") as f:
issues = [l for l in f if "Issue:" in l]
print("Security issues found:", len(issues))
20️⃣ Build Reproducibility
Purpose: Guarantee identical binaries from identical source—important for safety certification.
Approach Tools
checksum comparison sha256sum
Reproducible-builds.org guidelines diffoscope
Docker-based builds stable build environments
Example CI step
# Build twice and compare hashes
make clean && make && sha256sum app > hash1.txt
make clean && make && sha256sum app > hash2.txt
diff hash1.txt hash2.txt && echo "Reproducible build ✅"
Python script
import hashlib
def filehash(path):
h = hashlib.sha256()
with open(path,'rb') as f:
h.update(f.read())
return h.hexdigest()
print(filehash("build1/app"), filehash("build2/app"))
🏁 Integration Tips
Automate all commands in your CI/CD (GitHub Actions, GitLab CI, Jenkins).
Record metrics to a database (Prometheus, SonarQube) for trend analysis.
Set thresholds:
Peak memory < X MB,
0 data races,
perf regression < 5%,
0 known CVEs,
binary hashes identical.
These concrete methods, tools, and scripts let you continuously measure memory safety, concurrency robustness, performance, security, and reproducibility (items 16–20) in large mobility/automotive software projects.
Please provide specific methods, tools, and source code for measuring items 21 to 25.
ChatGPT:
Here are practical measurement approaches, recommended tools, and sample code for items 21 – 25 from the mobility-software intermediate-quality metrics list.
21️⃣ Logging & Diagnostic Coverage
Purpose: Ensure key events, errors, and states are properly logged for field diagnostics.
Technique Tools Notes
Static search for log() calls grep, custom script Count log density
Runtime log coverage Run tests → parse logs Verify expected events
Structured logging check pylint custom plugin / ESLint rule Enforce JSON/structured logs
Example (Python – check each function for logging)
import ast, pathlib
def has_logging(node):
return any(isinstance(n, ast.Call) and getattr(n.func, "attr", "") in {"info","error"}
for n in ast.walk(node))
total, with_log = 0, 0
for file in pathlib.Path("src").rglob("*.py"):
tree = ast.parse(open(file).read())
for fn in [n for n in ast.walk(tree) if isinstance(n, ast.FunctionDef)]:
total += 1
if has_logging(fn): with_log += 1
print(f"Logging coverage: {with_log}/{total} ({with_log/total:.1%})")
22️⃣ Configuration Management & Parameter Drift
Purpose: Ensure build/test configs remain reproducible and consistent.
Technique Tools
Hash/compare config files sha256sum, Python hashlib
Track environment (compiler, libs) pip freeze, conda env export, docker
Policy checks Ansible/Chef ansible-lint
Example (Python – detect drift)
import hashlib, json, pathlib
def hash_file(p):
h=hashlib.sha256()
with open(p,'rb') as f: h.update(f.read())
return h.hexdigest()
baseline = json.load(open("config_hashes.json"))
for f in pathlib.Path("config").rglob("*"):
if f.is_file():
h = hash_file(f)
if baseline.get(str(f)) != h:
print("Config changed:", f)
23️⃣ Portability / Cross-Platform Build Success
Purpose: Verify software compiles & runs across target OS/architectures (e.g., ARM vs x86).
Tools / Methods
CMake + Docker/Podman matrix
GitHub Actions matrix builds
cross (Rust), qemu for emulation
Example (GitHub Actions YAML snippet)
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
arch: [x86_64, arm64]
steps:
- uses: actions/checkout@v4
- name: Cross Build
run: |
docker buildx build --platform linux/${{ matrix.arch }} .
Local quick check:
qemu-aarch64 ./app_arm64 # run arm build on x86 host
24️⃣ Safety Mechanism Test Coverage (ISO 26262)
Purpose: Ensure fail-safe paths and safety mechanisms (watchdogs, redundancy) are tested.
Tools Approach
VectorCAST / Tessy Automotive standard MC/DC
Custom fault-injection tests Python/C++
Hardware-in-the-Loop (HIL) dSPACE, NI PXI
Example (C++ fault-injection unit test with GoogleTest)
#include
extern bool safety_shutdown(bool fault);
TEST(SafetyTest, InjectFault) {
EXPECT_TRUE(safety_shutdown(true)); // must trigger shutdown
}
Integrate with coverage:
lcov --capture --directory . --output-file safety.info
genhtml safety.info --output-directory out
25️⃣ Maintainability Index (MI)
Purpose: Quantify long-term maintainability from complexity, lines of code, and comment ratio.
Tools Language
radon (Python)
Visual Studio Code Metrics (C#, C/C++)
SonarQube Maintainability Rating
Formula (classic)
MI = 171 – 5.2ln(Halstead Volume) – 0.23Cyclomatic Complexity – 16.2ln(LOC) + 50sin(sqrt(2.4*Comment %))
Example (Python + radon)
pip install radon
radon mi src/ --json > mi.json
import json
data = json.load(open("mi.json"))
for file, score in data.items():
print(f"{file}: MI {score['mi']:.1f}")
✅ Integration Tips
Automate in CI/CD: run scripts at every merge.
Thresholds: e.g., Logging coverage ≥ 80 %, Maintainability Index ≥ 65.
Dashboards: Push JSON/CSV to Grafana, SonarQube, or custom web UI for trend tracking.
These concrete methods, tools, and code snippets enable continuous measurement of
logging diagnostics, config integrity, portability, safety-mechanism testing, and maintainability—items 21 – 25—for large mobility/automotive software projects.
あなた:
Please provide specific methods, tools, and source code for measuring items 26 to 32.
ChatGPT:
Here are practical measurement approaches, recommended tools, and example scripts for items 26 – 32 of the mobility-software intermediate-quality metrics list.
26️⃣ Build-Time & Size Trends
Goal: Track how long and how large each build becomes.
Technique Tools
Measure duration CI timestamps, time command
Track binary size ls -lh, du, size (ELF)
Trend graph Grafana, SonarQube custom metric
Example (bash + Python)
START=$(date +%s)
make
END=$(date +%s)
echo $((END-START)) > build_time.txt
stat -c%s build/output/app > build_size.txt
print("Build time:", int(open("build_time.txt").read()), "seconds")
print("Binary size:", int(open("build_size.txt").read())/1024, "KB")
27️⃣ Integration-Test Pass Rate
Goal: Ensure end-to-end tests remain stable.
Tool Notes
pytest, JUnit, GoogleTest Output JUnit XML
CI dashboard Parse pass/fail ratio
Example (Python / pytest)
pytest --junitxml=result.xml
import xml.etree.ElementTree as ET
tree = ET.parse('result.xml')
total = len(tree.findall('.//testcase'))
failed = len(tree.findall('.//failure'))
print(f"Pass rate: {(total - failed)/total:.1%}")
28️⃣ Defect Density (Bugs per KLOC)
Goal: Track discovered defects relative to code size.
Inputs Tools
Issue tracker (Jira, GitHub) count closed bugs
LOC cloc
Example
cloc src/ --json > cloc.json
import json, requests
Replace with your tracker’s API call:
bugs = requests.get("https://tracker/api/bugs?status=closed&since=30d").json()
loc = json.load(open("cloc.json"))["SUM"]["code"]
print("Defect density:", len(bugs)/(loc/1000), "bugs/KLOC")
29️⃣ Mean-Time-to-Repair (MTTR)
Goal: Measure average time to resolve defects.
Source Tools
Issue tracker GitHub Issues, Jira
Query creation & close timestamps API
Example (GitHub)
import requests, statistics, datetime
issues = requests.get("https://api.github.com/repos/ORG/REPO/issues?state=closed").json()
durations = []
for i in issues:
created = datetime.datetime.fromisoformat(i["created_at"].replace('Z',''))
closed = datetime.datetime.fromisoformat(i["closed_at"].replace('Z',''))
durations.append((closed - created).total_seconds()/3600)
print("MTTR (hours):", statistics.mean(durations))
30️⃣ Customer-Found Defect Rate
Goal: Track bugs reported from field/production vs. internal tests.
Technique Tools
Label external tickets “customer” tag in Jira/GitHub
Ratio external bugs / total releases
Example
customer = sum(1 for i in issues if "customer" in [l["name"] for l in i["labels"]])
print("Customer-found defect rate:", customer/len(issues))
31️⃣ Patch / Hot-Fix Frequency
Goal: Count emergency patches between planned releases.
Tools Approach
Git tags + semantic versioning count “.hotfix” tags
CI/CD release records query by date
Example (Git)
git tag --list "hotfix" | wc -l
from subprocess import check_output
hotfixes = check_output(["git","tag","--list","hotfix"]).decode().splitlines()
print("Hotfix count this quarter:", len(hotfixes))
32️⃣ Technical Debt Index
Goal: Quantify refactoring effort required to reach “ideal” code quality.
Tools Metric
SonarQube “Technical Debt” in hours
Code Climate Maintainability issues
DIY Weight open code-smell issues × estimated fix time
Example (SonarQube Web API)
import requests
r = requests.get("https://sonar/api/measures/component",
params={"component":"project","metricKeys":"sqale_index"})
print("Technical debt (min):", r.json()["component"]["measures"][0]["value"])
Integration Tips
Automate all scripts in CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins).
Dashboards: Store JSON/CSV outputs in Grafana, ELK, or SonarQube for trend charts.
Thresholds/Policies:
Build < 10 min
Integration pass rate ≥ 95 %
Defect density ≤ 0.5 / KLOC
MTTR ≤ 48 h
Technical debt < X days
These methods, tools, and code snippets let you continuously measure build efficiency, testing effectiveness, defect handling, and long-term maintainability (items 26–32) for large mobility/automotive software projects.
It seems like you're leaning towards a two-party approach to design based on requirements. Please change it to a description based on design goals.