Test Data Management

# Test data manifest: ties test suite to specific data versions
test_suite: "AEB_Regression_v2.3"
created: "2025-01-15"
author: "test.engineer@tier1.com"

data_dependencies:
  dbc_file:
    path: "signal_databases/vehicle_v4.2.dbc"
    sha256: "a3f8c2d1e4b7..."
    ecu_sw_version: "AEB_SW_v4.2.0"

  test_vectors:
    path: "test_data/aeb_vectors_v3.1.csv"
    sha256: "f7e9a1b2c5d4..."
    change_reason: "Added high-speed partition per SWR-AEB-015"

  calibration:
    path: "calibration/aeb_cal_v4.2.0.a2l"
    sha256: "d2c8f3a7b1e5..."

validation_rules:
  - "DBC must match ECU SW version"
  - "All test vectors must reference valid req_id"
  - "Calibration file hash must match ECU flash image metadata"

"""Auto-generate test vectors from requirement specifications."""
import itertools
from dataclasses import dataclass
from typing import List

@dataclass
class SignalRange:
    name: str
    partitions: list  # [(name, min, max, representative_value)]

def generate_pairwise_vectors(
        signals: List[SignalRange],
        req_prefix: str = "SWR") -> List[dict]:
    """Generate pairwise test vectors (all 2-way combinations)."""
    # Get representative values per partition per signal
    signal_values = []
    for sig in signals:
        values = [(sig.name, p[3], p[0]) for p in sig.partitions]
        signal_values.append(values)
    # Pairwise: every pair of signals covered in at least one test
    vectors = []
    for combo in itertools.product(*signal_values[:2]):
        vec = {item[0]: item[1] for item in combo}
        vec["partition_labels"] = [item[2] for item in combo]
        vectors.append(vec)
    return vectors