Use Cases

TRF is applied across industries whenever teams need defensible evidence chains. The examples below show how the same format supports safety, cybersecurity, AI/ML governance, and multi-tier supply chains without reinventing tooling.

Automotive: Adaptive Cruise Control (ACC)

Context

500+ safety and functional requirements scattered across 15 documents.
Evidence stored in five tools (requirements, tests, simulations, hardware-in-the-loop, safety analysis).
Three-week audit preparation cycle exposed 40% traceability gaps.
Compliance targets: ISO 26262 ASIL‑C, UN‑R155, OEM-specific policies, multiple regional regulations.

TRF package excerpt

{
  "profile": "automotive-safety",
  "extensions": ["iso26262", "unr155"],
  "artifacts": [
    { "kind": "safety_requirement", "asil": "C", "id": "SR-ACC-001" },
    { "kind": "hazard", "severity": 3, "exposure": 3, "id": "HZ-ACC-014" },
    { "kind": "threat", "stride": "tampering", "id": "TH-ACC-006" },
    { "kind": "test_result", "verdict": "passed", "id": "TR-ACC-021" }
  ]
}

Impact

100% requirement coverage verified with automated trace matrices.
Audit preparation reduced from three weeks to two hours.
Change impact analysis executed instantly across safety and cybersecurity artifacts.
ASIL decomposition rules enforced automatically.

Cybersecurity: OTA Update Campaign

Context

Vehicle manufacturer responding to vulnerability CVE-2024-1234 affecting ECU ADAS-v2.3.1.
Must meet UN‑R155/156 evidence requirements for update tracking, SBOM, and incident reporting.

TRF campaign artifact

{
  "kind": "ota_campaign",
  "id": "OTA-2024-001",
  "vulnerability": {
    "cve": "CVE-2024-1234",
    "severity": "HIGH",
    "affected_components": ["ECU-ADAS-v2.3.1"]
  },
  "deployment": {
    "target_vehicles": 125000,
    "deployed": 124850,
    "success_rate": 0.9988
  },
  "verification": {
    "pre_deployment_tests": "passed",
    "post_deployment_validation": "passed"
  }
}

Impact

Precisely targeted 125,000 vehicles, averting roughly $2 million in unnecessary updates.
Real-time dashboards tracked deployment progress and validation status.
Generated complete regulatory evidence, paired with cryptographic proof of delivered updates.

AI/ML: Autonomous Emergency Braking (AEB)

Context

Must demonstrate safety across operational design domain scenarios, provide regulator-ready explanations, capture edge cases, and manage continuous model improvements.

TRF structure

{
  "artifacts": [
    {
      "kind": "dataset",
      "id": "DS-AEB-01",
      "size": "10TB",
      "scenarios": 50000,
      "edge_cases": 500,
      "validation_split": 0.2
    },
    {
      "kind": "model",
      "id": "MODEL-AEB-3.2.0",
      "architecture": "CNN+LSTM",
      "training_hours": 2400,
      "performance_metrics": {
        "precision": 0.9987,
        "recall": 0.9991,
        "f1_score": 0.9989
      }
    },
    {
      "kind": "odd",
      "weather": ["clear", "rain", "fog"],
      "speed_range": [0, 130],
      "road_types": ["highway", "urban", "rural"]
    },
    {
      "kind": "runtime_monitor",
      "constraints": ["max_deceleration", "min_distance"],
      "fallback": "traditional_AEB"
    }
  ]
}

Impact

End-to-end ML pipeline traceability connects datasets, experiments, and runtime monitors.
Regulators view dataset-to-performance linkage and safety cage parameters without custom exports.
Model updates are validated rapidly with automated regression and edge-case coverage checks.

Multi-Tier Supply Chain Integration

Context

OEM combines Tier‑1 ADAS integrator, Tier‑2 sensor suppliers (radar, camera, lidar), and Tier‑3 chip manufacturers.
Tools vary: IBM DOORS, Jama Connect, and custom XML systems.

Flow of TWPacks

Integration record

{
  "embedded_packs": [
    { "supplier": "RadarCorp", "pack": "radar-v2.1.twpack" },
    { "supplier": "CamVision", "pack": "camera-v3.0.twpack" },
    { "supplier": "LidarTech", "pack": "lidar-v1.5.twpack" }
  ],
  "interface_validation": [
    { "from": "radar:OUTPUT-001", "to": "adas:INPUT-001" },
    { "from": "camera:OUTPUT-002", "to": "adas:INPUT-002" }
  ],
  "integration_tests": [
    { "scenario": "sensor_fusion", "result": "passed" },
    { "scenario": "failover", "result": "passed" }
  ]
}

Impact

Continuous visibility from Tier‑3 silicon to OEM integration.
Automated interface validation reduced integration issues by 60%.
Supplier audits shortened from three weeks to three days.

ASPICE Level 3 Compliance

Context

Targeting Automotive SPICE Level 3: requires bidirectional traceability, standardized processes, quantitative management, and continuous improvement proof.

Traceability matrix snapshot

{
  "levels": {
    "customer": ["CUST-REQ-001", "CUST-REQ-002"],
    "system": ["SYS-REQ-001", "SYS-REQ-002"],
    "software": ["SW-REQ-001", "SW-REQ-002"],
    "architecture": ["ARCH-001", "ARCH-002"],
    "detailed_design": ["DD-001", "DD-002"],
    "implementation": ["CODE-001", "CODE-002"],
    "unit_test": ["UT-001", "UT-002"],
    "integration_test": ["IT-001", "IT-002"],
    "system_test": ["ST-001", "ST-002"]
  },
  "coverage": {
    "forward": 0.98,
    "backward": 0.97,
    "horizontal": 0.99
  }
}

Impact

Achieved ASPICE Level 3 in six months (typical timeline: 18 months).
Coverage metrics generated automatically for each assessment.
Continuous monitoring improved productivity by 15%.

Incident Response: Field Issue Investigation

Context

Lane-keeping assist failure reported in production vehicles under specific environmental conditions.
Safety-critical root cause analysis required with regulator visibility.

Investigation pack

{
  "incident": {
    "id": "INC-2024-0342",
    "severity": "HIGH",
    "reported": "2024-02-15T09:30:00Z"
  },
  "investigation_chain": [
    { "artifact": "field_report:FR-2024-0342" },
    { "artifact": "test_result:ST-LKA-234", "status": "passed" },
    { "artifact": "requirement:REQ-LKA-015", "gap": "found" },
    { "artifact": "hazard:HAZ-LKA-003", "unmitigated": true }
  ],
  "root_cause": {
    "description": "Edge case not covered in requirements",
    "conditions": ["wet roads", "worn lane markings", "sunset glare"]
  },
  "corrective_actions": [
    { "action": "Update requirements", "id": "CA-001" },
    { "action": "Add test scenario", "id": "CA-002" },
    { "action": "Software update", "id": "CA-003" }
  ]
}

Impact

Identified root cause in four hours (previously two weeks).
Produced a complete investigation trail for regulators.
Delivered targeted fix and prevented recurrence.

Startup: First ISO 26262 Certification

Context

EV startup entering regulated market with no legacy processes, limited staff, and aggressive schedule.

TRF-first plan

{
  "week_1_2": "Define safety requirements in TRF",
  "week_3_4": "Link requirements to design",
  "month_2": "Plan tests with coverage targets",
  "month_3": "Track implementation and reviews",
  "month_4": "Execute validation and verification",
  "month_5": "Generate certification TWPack"
}

Impact

Achieved certification in five months (industry norm: 12–18 months).
Saved an estimated $2 million in consultant costs.
Passed audit with zero non-conformances, establishing competitive credibility.

Medical Device: Insulin Pump Program

Context

Medical device manufacturer preparing FDA 510(k) submission while aligning with IEC 62304 and ISO 14971.

Adapted profile

{
  "profile": "medical-device",
  "standards": ["FDA-510k", "IEC-62304", "ISO-14971"],
  "artifacts": [
    { "kind": "clinical_requirement", "classification": "Class_C" },
    { "kind": "hazard", "harm": "hypoglycemia", "severity": "critical" },
    { "kind": "clinical_test", "protocol": "CT-2024-001" },
    { "kind": "risk_control", "measure": "redundant_glucose_sensor" }
  ]
}

Impact

Produced a single evidence source for FDA reviewers.
Maintained a complete audit trail across software, clinical, and risk artifacts.
Cut approval time by about 40% and ended the audit with zero FDA observations.

Future Vision: Smart City Infrastructure

Context

Municipal programs coordinating traffic management, connected infrastructure, V2X communication, and city-wide OTA updates.

Concept pack

{
  "scope": "city-wide",
  "systems": [
    "traffic_lights",
    "connected_vehicles",
    "emergency_services",
    "public_transport"
  ],
  "integration_points": 1250,
  "real_time_monitoring": true,
  "citizens_affected": 2000000
}

Potential

Align disparate infrastructure providers under one verifiable evidence format.
Support city-wide governance for safety, cybersecurity, and mobility outcomes.

Key Takeaways

Pilot TRF on a high-value system, then automate TWPack generation through CI/CD and iterate on coverage metrics.
Industries actively using TRF include automotive, aerospace, medical devices, rail, industrial automation, and robotics.
Typical ROI timeline:

Next steps

Understand core artifacts.
Dive into modular architecture.
Build your first package with Creating Your First Pack.
Map TRF to standards via Compliance Mapping.

Automotive: Adaptive Cruise Control (ACC)​

Cybersecurity: OTA Update Campaign​

AI/ML: Autonomous Emergency Braking (AEB)​

Multi-Tier Supply Chain Integration​

ASPICE Level 3 Compliance​

Incident Response: Field Issue Investigation​

Startup: First ISO 26262 Certification​

Medical Device: Insulin Pump Program​

Future Vision: Smart City Infrastructure​

Key Takeaways​

Automotive: Adaptive Cruise Control (ACC)

Cybersecurity: OTA Update Campaign

AI/ML: Autonomous Emergency Braking (AEB)

Multi-Tier Supply Chain Integration

ASPICE Level 3 Compliance

Incident Response: Field Issue Investigation

Startup: First ISO 26262 Certification

Medical Device: Insulin Pump Program

Future Vision: Smart City Infrastructure

Key Takeaways