hil-testgen 0.1.0

Automatically generate structured HIL/SIL/MIL test cases from requirements documents locally

hil_testgen

Open-source Python package that automatically generates structured test cases from requirements documents.

hil-testgen

Generate structured HIL/SIL/MIL test cases from requirements documents. 100% local. NDA-safe. No data leaves your machine.

pip install hil-testgen

---

What it does

HIL test engineers spend hours manually converting requirements documents into structured test cases in Excel. hil-testgen automates that.

Input — a requirements document (.docx, .xlsx, .csv, .pdf)

Output — a structured test case document with:

• Test Case ID linked to Requirement ID (traceability)
• Preconditions, Input Signals, Test Steps
• Pass Criteria with exact thresholds preserved
• Fail Criteria
• Priority and Test Type classification
• Confidence scoring (HIGH / REVIEW / LOW)

All processing happens locally using Ollama. No data is sent to any external server.

---

Quick start

1. Install hil-testgen

pip install hil-testgen

2. Install Ollama

Download from ollama.ai and install.

ollama pull llama3

3. Run

hil-testgen generate requirements.docx

That's it. A test_cases.xlsx file is saved in your current directory.

---

Usage

Command line

# Basic — generates Excel output
hil-testgen generate requirements.docx

# HTML report (visual, opens in browser)
hil-testgen generate requirements.docx --format html

# CSV output
hil-testgen generate requirements.docx --format csv

# Custom output path
hil-testgen generate requirements.docx --output my_tests.xlsx

# Use a different Ollama model
hil-testgen generate requirements.docx --model mistral

# Verbose mode — see detailed logs
hil-testgen generate requirements.docx --verbose

# Check your setup
hil-testgen info

Python API

from hil_testgen import generate

# Minimal — generates Excel in current directory
generate("requirements.docx")

# Full options
generate(
    requirements_file="requirements.docx",
    output="test_cases.xlsx",
    export_format="excel",  # "excel", "csv", "html"
    model="llama3",
    verbose=False
)

---

Output formats

Excel (.xlsx) — default

Three tabs:

• Generated Test Cases — color coded by confidence
• Needs Review — skipped requirements with exact reasons
• Summary — stats at a glance

HTML (.html)

• Expandable cards per test case
• Color coded HIGH / REVIEW / LOW confidence badges
• Stats dashboard at top
• Opens in any browser — no dependencies needed

CSV (.csv)

• Flat file, one row per test case
• Separate _skipped.csv for requirements that need attention

---

Supported input formats

Format	Extension	Notes
Word	.docx	Best support — recommended
Excel	.xlsx	Auto-detects column layout
CSV	.csv	Simple tabular format
PDF	.pdf	Best effort — convert to .docx if results are poor

Note: If your requirements are in a format not listed above (IBM DOORS, Polarion, Confluence), export or copy them into a .docx file first. Most tools support this in one click.

---

Supported test environments

hil-testgen was designed for automotive ECU validation but works for any requirements document structured around objectives, test methods, and pass criteria:

• HIL (Hardware-in-the-Loop)
• SIL (Software-in-the-Loop)
• MIL (Model-in-the-Loop)
• PIL (Processor-in-the-Loop)

---

Why local?

Automotive requirements documents contain proprietary ECU designs, calibration data, and safety-critical specifications covered by NDA.

Sending these to a cloud AI API (ChatGPT, Gemini, Claude) risks:

• NDA violation
• IP exposure
• Company security policy breach

hil-testgen uses Ollama to run AI inference entirely on your machine. Nothing ever leaves your laptop.

Zero network calls. Zero data exposure.

---

Confidence scoring

Every generated test case is scored based on how complete the original requirement was:

Level	Score	Meaning
HIGH	80-100	Requirement was complete — test case ready to use
REVIEW	50-79	Some fields were missing — verify before use
LOW	0-49	Significant info missing — AI inferred values

---

Example

Given this requirement: HIL-RQ1: System Initialization Objective: Verify correct initialization of start positions Test Method: Load known values for Const_Offset_Lat, Startposition_Latitude. Observe Veh_Pos_Lati output. Pass Criteria: Output GPS coordinates match within ±0.00001°

hil-testgen generates: TC ID → TC_HIL-RQ1_001 Requirement ID → HIL-RQ1 Title → Initialization of Start Positions Preconditions → ECU in default state, no prior test runs Input Signals → Const_Offset_Lat, Startposition_Latitude Test Steps → 1. Load known values 2. Observe Veh_Pos_Lati and Veh_Pos_Longi outputs Pass Criteria → Output GPS coordinates match within ±0.00001° Fail Criteria → Output does not match within ±0.00001° Priority → MEDIUM Test Type → Functional Confidence → HIGH

---

Disclaimer

All generated test cases must be reviewed by a qualified engineer before use in a validation environment. hil-testgen assists with drafting — it does not replace engineering judgment.

Generated test cases represent a structured starting point. Signal values, calibration parameters, and environment-specific details must be verified and completed by the test engineer.

---

Roadmap

• Retry logic for failed AI generations
• Better numeric value extraction from requirements
• CAPL script export
• pytest script export
• ReqIF / DOORS import support
• Multi-language requirements support

---

Contributing

Contributions welcome! Please open an issue first to discuss what you'd like to change.

---

License

MIT — free to use, modify, and distribute.

---

Built for HIL/SIL/MIL test engineers who are tired of staring at blank Excel sheets. 🚗