Generate structured HIL/SIL/MIL test cases from requirements documents using local AI — NDA safe, 100% offline
Project description
hil-test-tool
Generate structured HIL/SIL/MIL test cases from requirements documents using local AI — 100% offline, NDA safe.
pip install hil-test-tool
Why
Writing HIL test cases manually from requirements documents is tedious and error-prone. This tool reads your requirements file, sends each requirement to a local AI model (via Ollama), and generates a structured Excel test matrix in the standard HIL test case format.
100% local inference — your requirements never leave your machine.
Requirements
- Python 3.9+
- Ollama running locally
- Mistral pulled:
ollama pull mistral
Install
pip install hil-test-tool
Usage
# Generate test matrix from a requirements document
hil-tool generate GPS_Requirements.docx
# Use a different model
hil-tool generate requirements.docx --model mixtral
# Generate for specific requirements only
hil-tool generate requirements.docx --req HIL-RQ1 --req HIL-RQ5
# Custom output filename
hil-tool generate requirements.docx --output GPS_TestMatrix.xlsx
# Custom sheet title
hil-tool generate requirements.docx --title "GPS Block — HIL Test Cases"
# List available Ollama models
hil-tool models
# Show version and config
hil-tool info
Output format
Single sheet: TestCases_SystemRequirement
| Test Case ID | Requirement Ref | Description | Variant Conditions (Inputs) | Expected Output | Pass Criteria | Result |
|---|---|---|---|---|---|---|
| TC-GPS-001 | HIL-RQ1 | Verify initialization... | Pre: ... Inputs: ... Monitor: ... | Expected value... | Within ±0.00001° |
Test cases are grouped by requirement with GROUP header rows. Result column is left blank for manual fill after test execution.
Supported input formats
.docx— Word requirements documents.xlsx— Excel requirements sheets
Model recommendations
| Model | Quality | Speed | Use case |
|---|---|---|---|
| mistral (default) | Good | Fast | Standard use |
| mixtral | Excellent | Slow | Complex requirements |
| llama3 | Fair | Fast | Fallback |
Design principles
- Never guess — the AI only uses signal names and values explicitly stated in the requirements
- Skip, don't crash — if a requirement fails after 3 attempts, it is skipped and reported
- NDA safe — Ollama runs locally, nothing is sent to any cloud API
Planned (v0.2)
- Test vector output for manual testing
- Python pytest script generation
- dSPACE integration
- ReqIF format support
Project details
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