Generate standardized, traceable automotive test cases from requirements documents and export them as structured, review-ready Excel workbooks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install automotive-testcase-generator
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install automotive-testcase-generator using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Automotive Test Case Generator?
Automotive Test Case Generator is an Openclaw Skills capability for automotive electronics and intelligent-vehicle testing. It converts Markdown, PDF, Word, Excel, or plain-language requirements into structured test cases covering ECU behavior, CAN/CAN FD, LIN, FlexRay, automotive Ethernet, SOME/IP, UDS, OBD, DTC, OTA, ADAS, HIL, functional safety, cybersecurity, EMC, and environmental reliability.
The skill emphasizes traceability, measurable expected results, standards compliance, module-based organization, priority classification, and systematic coverage of normal, boundary, fault-injection, recovery, and exploratory scenarios. It produces an Excel workbook with module separators, priority coloring, detailed test fields, and coverage statistics. It generates test documentation only; it does not execute tests or produce automation scripts.
Automotive Test Case Generator Use Cases
- Convert automotive product requirements into review-ready ECU and vehicle test cases.
- Generate CAN and automotive network tests for periodic messages, timeouts, lost frames, invalid frames, CRC errors, and bus recovery.
- Design UDS and OBD diagnostic cases for session control, security access, DTC reading and clearing, negative responses, and lockout behavior.
- Build OTA upgrade test coverage for version checks, interrupted downloads, power loss, rollback, signature validation, and multi-ECU coordination.
- Create ADAS scenario tests across object types, speeds, distances, weather, lighting, road geometry, sensor failures, and boundary trigger conditions.
- Cover functional safety, ISO 26262, ASIL-related mechanisms, cybersecurity, ISO 21434, SecOC, authentication, encryption, EMC, environmental, and reliability requirements.
- Create regression-ready test suites linked to modules and original requirement sources.
- Process one document, small batches of two to three documents, or larger batches while preserving global module context.
- Identify unclear or non-testable requirements and mark them for clarification instead of inventing unsupported test cases.
- Use Openclaw Skills to standardize test design across automotive teams without mixing in robotics, industrial, or computer-vision testing domains.
How Automotive Test Case Generator Works
- Determine the input mode: single document or requirement description, small batch, or batch input with more than three documents.
- For batch input, scan all documents once and create a shared automotive module map containing domain relationships, signals, services, state transitions, dependencies, and source-file metadata.
- Identify modules at the domain-controller or subsystem level, such as CSN, IVI, CLU, BCM, PWR, CHS, ADAS, GW, TBOX, OTA, DIAG, NET, FUSA, and CSEC. Assign a unique uppercase prefix of up to four letters to new modules.
- Extract every verifiable requirement and classify its complexity sources, quality attributes, referenced standards, module, source document, and testability status.
- Mark requirements as testable, non-testable, or requiring clarification. Use explicit assumption and hidden-item markers where domain expectations need confirmation.
- Map each module to a business layer: core, important, auxiliary, or foundational. Safety-related modules such as braking, steering, and airbags are treated as core modules.
- Generate test cases using the mandatory four-layer model: P0 main flows, P1 business and signal details, P2 black-box methods and fault analysis, and P3 exploratory or extreme scenarios.
- Apply at least three distinct test-design methods per module, selecting techniques such as boundary-value analysis, equivalence partitioning, state transition, decision tables, scenario testing, fault injection, error guessing, and pairwise testing.
- Add automotive-specific abnormal scenarios, including input, process, environmental, authorization, data, and state abnormalities. Include fault injection and safe-state behavior for safety-related areas.
- Generate concrete test data, such as speed, voltage, temperature, CAN periods, message identifiers, DTC values, invalid payloads, and special values. Clearly label synthetic test data and anonymize real vehicle information.
- Enforce coverage requirements for dimensions, priorities, test levels, regression cases, and standards-derived test items. Standards-referenced requirements must achieve 100% test-item coverage.
- Render the approved cases into a two-sheet Excel workbook containing the test cases and coverage statistics, then run the final blocking checklist before delivery.
Automotive Test Case Generator Setup
Prerequisites
The skill requires Python 3 and the openpyxl package for Excel generation. The AI workflow itself performs module recognition, requirement extraction, test design, and validation without requiring a separate test-execution framework.
Install the Excel dependency
python3 --version
python3 -m pip install openpyxl
Prepare the skill references
Keep the skill reference files available in the skill directory:
references/domain-knowledge.md— automotive protocols, standards, methods, tools, risks, and environment templates. Read before every generation.references/format-spec.md— authoritative test-case fields, JSON structure, and Excel output specification. Read before generating cases and exporting Excel.examples/— optional requirements and workbook examples for demonstrations and acceptance review.
Provide input
Supply one or more automotive requirement sources in Markdown, PDF, Word, Excel, or plain text, or describe the requirements directly. For batch work, provide more than three documents so the workflow can build a shared module map before processing each document independently.
Output expectations
The final deliverable is a structured .xlsx workbook generated with Python and openpyxl. Confirm that the output path is writable and that any real vehicle data has been anonymized. This Openclaw Skills workflow creates test specifications; it does not execute tests or generate CAPL, CANoe, or other automation scripts.
Automotive Test Case Generator Data Schema & Taxonomy
Module map
Each module record includes:
- Module name and unique prefix, such as
CSNorDIAG - Business layer: core, important, auxiliary, or foundational
- Core functions
- Key CAN signals, network services, or diagnostic services
- Power, sleep, wake, and lifecycle state transitions
- Related modules and cross-domain dependencies
- Minimum required case count
- Source file in batch mode
Batch processing stores the shared result as phase1_modules.json, including a source file field for traceability.
Requirement records
Requirement extraction organizes each item with:
| Field | Purpose |
|---|---|
| Requirement ID | Stable identifier such as REQ-CSN-001 |
| Description | Verifiable requirement statement |
| Module | Owning automotive subsystem or domain |
| Complexity sources | Input space, state combinations, logic, process steps, or configuration combinations |
| Quality attributes | Function, network/protocol, diagnostics, performance, reliability, functional safety, cybersecurity, EMC, compatibility, usability, or compliance |
| Referenced standards | Standard number and year, or [no standard reference] |
| Testability | Testable, non-testable, or clarification required |
| Source file | Original requirement document |
Test-case schema
The fixed format contains: test case ID, business module, priority, test dimension, case type, design method, scenario, test point, 3–5 operation steps, test data, preconditions, requirement source, bus/signal information, test environment, and test level.
Taxonomy and output
- Priorities: P0, P1, P2, and P3, with red, orange, green, and gray Excel styling respectively.
- Test levels: unit, integration, system, and acceptance.
- Execution environments: MIL/SIL, HIL, bench, and real vehicle.
- Abnormality classes: input, process, environment, authorization, data, and state.
- Workbook sheets:
Test Caseswith 15 columns andCoverage Statisticscontaining module, dimension, standard, test-item, and case-count coverage. - Module sections are separated by rows formatted as
【Module Name】, while multi-step operations use line breaks within cells.
Automotive Test Case Generator Advanced Features
- Global module mapping for multi-document batches, including cross-domain signal and bus dependencies.
- Strict four-layer generation with blocking completion checks between P0/P1, P2, and P3 coverage stages.
- Automotive-specific design-method selection based on complexity sources, including boundary analysis, equivalence classes, state transitions, decision tables, scenarios, fault injection, error guessing, and pairwise combinations.
- Standards-to-test-item traceability for GB/T, QC/T, OEM specifications, ISO 26262, ISO 21434, ISO 11452, CISPR 25, ISO 7637, ISO 16750, and related compliance references.
- Hard 100% coverage enforcement for referenced standards and individual standard test items, with missing cases added before delivery.
- Safety-focused generation for failure injection, degradation, safe-state entry, watchdog or ECC mechanisms, sensor failures, and response-time validation.
- Dedicated templates for CAN robustness, UDS diagnostics, OTA upgrades, and ADAS scenario combinations.
- Priority distribution and minimum-count controls based on module criticality, including elevated treatment for safety modules.
- Regression support through requirement-source links, per-module regression cases, P0 smoke suites, and change-driven filtering by module and source requirement.
- Concrete synthetic test-data generation for boundaries, valid equivalence classes, invalid values, payload errors, DTCs, voltage, speed, and temperature.
- Final delivery gate that blocks export when traceability, method diversity, abnormal-scenario coverage, test-level coverage, standards coverage, or regression requirements are incomplete.
- Openclaw Skills integration is intentionally domain-scoped: the capability focuses on automotive test-case documentation and does not perform test execution, automation-script generation, or unrelated robotics and industrial workflows.
SKILL.md
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