What Is Design Validation Testing

What Is Design Validation Testing with TofuPilot

Design validation testing (DVT) confirms that a product meets its design requirements under real-world conditions. It happens after prototyping and before production tooling. This guide explains where DVT fits in the product development cycle, how to build DVT scripts in Python, and how to track validation results with TofuPilot.

Where DVT Fits

Hardware products typically go through three build stages:

DVT answers a specific question: does this design meet the requirements in the spec? You test against the product requirements document (PRD), not against manufacturing tolerances. The units are built on near-final tooling, and the test conditions include environmental extremes, mechanical stress, and electrical corner cases.

What DVT Covers

DVT is not pass/fail in the EOL sense. It produces data that feeds design decisions. A measurement that lands close to a limit might pass DVT but prompt a design change to add margin before PVT.

Prerequisites

• Python 3.10+
• OpenHTF installed (pip install openhtf)
• TofuPilot Python SDK installed (pip install tofupilot)

Step 1: Define Validation Measurements

DVT phases often measure the same parameter under different conditions. Use descriptive measurement names that include the test condition.

import openhtf as htf
from openhtf.util import units


@htf.measures(
    htf.Measurement("current_draw_25C_mA")
    .in_range(minimum=40, maximum=60)
    .with_units(units.MILLIAMPERE),
    htf.Measurement("current_draw_85C_mA")
    .in_range(minimum=40, maximum=70)
    .with_units(units.MILLIAMPERE),
)
def phase_thermal_current(test):
    """Measure current draw at room temp and high temp."""
    test.measurements.current_draw_25C_mA = 48.3
    test.measurements.current_draw_85C_mA = 55.1

Step 2: Add Marginal Limits

DVT benefits from marginal limits. A measurement inside the marginal band passes but flags a potential risk. TofuPilot tracks marginal results separately so you can spot trends before they become failures.

@htf.measures(
    htf.Measurement("output_voltage_V")
    .in_range(
        minimum=3.0, maximum=3.6,
        marginal_minimum=3.1, marginal_maximum=3.5,
    )
    .with_units(units.VOLT),
)
def phase_output_voltage(test):
    """Validate output voltage is within spec with margin."""
    test.measurements.output_voltage_V = 3.28

Step 3: Run and Track Results

Connect the DVT script to TofuPilot. Each unit in the DVT build gets its own test record, making it easy to compare performance across the build lot.

from tofupilot.openhtf import TofuPilot

test = htf.Test(
    phase_thermal_current,
    phase_output_voltage,
)

with TofuPilot(test):
    test.execute(test_start=lambda: input("Scan DVT unit serial: "))

Step 4: Analyze in TofuPilot

TofuPilot tracks DVT results automatically. Open the Analytics tab to review:

• Measurement distributions across the DVT build lot
• Marginal results flagged separately from hard failures
• Unit-level traceability linking each prototype to its full test history
• Trends over time to catch drift across sequential builds

This data feeds directly into the DVT report. Instead of manually compiling spreadsheets, export the data from TofuPilot or share the dashboard link with your team.

DVT vs Production Testing

DVT limits often start wider and tighten as you move into PVT and production. TofuPilot lets you track this evolution by comparing measurement distributions across test procedures for the same product.