1. Overview
2. Quality Engineering Architecture
3. Development Environment
4. Testing Framework
5. Test Suite Organization
6. Unit Testing
7. Automatic Differentiation Testing
8. Numerical Gradient Verification
9. Neural Network Testing
10. Optimizer and Loss Testing
11. Data Pipeline Testing
12. Integration Testing
13. Backend and Device Testing
14. ONNX and Serialization Testing
15. Performance Regression Testing
16. Code Coverage
17. Static Analysis and Formatting
18. Continuous Integration
19. Security and Dependency Quality
20. Documentation Testing
21. Cross-Platform Compatibility
22. Test Reproducibility
23. Quality Gates and Release Criteria
24. Troubleshooting
25. Contributor Testing Checklist
26. Developer References
27. Next Steps

Testing and quality assurance ensure that EveryO behaves correctly across its supported functionality.

For a machine learning framework, correctness extends beyond successful execution. Mathematical gradients, numerical stability, model behavior, and serialization compatibility must also be verified.

• Functional correctness.
• Mathematical correctness.
• Numerical stability.
• API reliability.
• Cross-platform compatibility.
• Reproducible behavior.
• Regression prevention.
• Secure development.
• Release confidence.

                    SOURCE CODE
                         |
                         v
                  Static Analysis
                         |
                         v
                    Unit Tests
                         |
                         v
                Numerical Validation
                         |
                         v
                 Integration Tests
                         |
                         v
                  Backend Tests
                         |
                         v
                Documentation Tests
                         |
                         v
                 Package Validation
                         |
                         v
                  Release Review

Every meaningful code change should be accompanied by tests appropriate to its risk.

For example, changing tensor broadcasting may require unit tests, gradient tests, shape edge cases, and integration tests.

git clone https://github.com/krishanth7/EveryO.git

cd EveryO

python -m venv .venv

Activate on Linux or macOS:

source .venv/bin/activate

Activate on Windows PowerShell:

.venv\Scripts\Activate.ps1

python -m pip install --upgrade pip

python -m pip install -e ".[dev]"

The inspected development extra includes:

• pytest
• pytest-cov
• Ruff
• PyYAML

python -m everyo info

python -m everyo doctor

EveryO uses pytest.

python -m pytest

python -m pytest -q

python -m pytest tests/test_autograd.py

python -m pytest \
  tests/test_autograd.py::TestBasicGradients::test_square_gradient

python -m pytest -x

python -m pytest -v

The repository declares:

[tool.pytest.ini_options]
minversion = "7.0"
testpaths = ["tests"]
addopts = "-q --strict-markers"

The configuration also treats RuntimeWarning as an error.

The inspected repository contains dedicated test files for multiple framework subsystems.

tests/
│
├── conftest.py
├── test_autograd.py
├── test_operations.py
├── test_layers.py
├── test_losses.py
├── test_optimizers.py
├── test_dataloader.py
├── test_integration.py
├── test_attention.py
├── test_conv.py
├── test_normalization.py
├── test_devices.py
├── test_distributed.py
├── test_precision.py
├── test_profiler.py
├── test_onnx.py
└── test_onnx_trace.py

This is a representative selection, not an exhaustive directory listing.

Unit tests verify individual operations in isolation.

import numpy as np
import everyo as eo

def test_tensor_addition():
    a = eo.tensor([1.0, 2.0])
    b = eo.tensor([3.0, 4.0])

    result = a + b

    np.testing.assert_allclose(
        result.numpy(),
        [4.0, 6.0]
    )

def test_matrix_multiplication_shape():
    a = eo.ones(3, 4)
    b = eo.ones(4, 5)

    result = eo.matmul(a, b)

    assert result.shape == (3, 5)

A good unit test should:

• Have a clear purpose.
• Use small deterministic inputs.
• Check meaningful outputs.
• Cover relevant edge cases.
• Produce actionable failure messages.

Automatic differentiation is a critical component of EveryO.

For:

[ f(x)=x^2 ]

the derivative is:

[ f'(x)=2x ]

At (x=2):

[ f'(2)=4 ]

import numpy as np
import everyo as eo

def test_square_gradient():
    x = eo.tensor(
        [2.0],
        requires_grad=True
    )

    y = x * x
    y.backward()

    np.testing.assert_allclose(
        x.grad,
        [4.0]
    )

This test corresponds to an existing gradient test pattern in EveryO.

• Addition.
• Subtraction.
• Multiplication.
• Division.
• Chain rule.
• Broadcasting.
• Gradient accumulation.
• Reused tensors.
• Gradient reset.
• Matrix multiplication.

Analytical gradients should be compared against numerical approximations.

For scalar-valued (f):

[ \frac{\partial f}{\partial x_i} \approx \frac{f(x_i+\epsilon)-f(x_i-\epsilon)} {2\epsilon} ]

where (\epsilon) is a small perturbation.

import numpy as np

def numerical_gradient(fn, values, epsilon=1e-5):
    values = np.asarray(
        values,
        dtype=np.float64
    ).copy()

    gradient = np.zeros_like(values)

    for index in np.ndindex(values.shape):
        original = values[index]

        values[index] = original + epsilon
        positive = fn(values)

        values[index] = original - epsilon
        negative = fn(values)

        values[index] = original

        gradient[index] = (
            positive - negative
        ) / (2.0 * epsilon)

    return gradient

np.testing.assert_allclose(
    analytical_gradient,
    numerical_gradient_result,
    rtol=1e-4,
    atol=1e-6
)

These tolerances are consistent with the inspected autograd test configuration, but should be adjusted when numerical conditions require it.

Finite differences may be unreliable near:

• Discontinuities.
• Non-differentiable points.
• Numerical overflow.
• Severe cancellation.
• Poorly scaled inputs.

Neural network layers should be tested independently and in combinations.

• Correct output shape.
• Parameter initialization.
• Forward computation.
• Backward gradients.
• Batch handling.
• Evaluation behavior.
• Numerical stability.

import everyo as eo

def test_linear_output_shape():
    layer = eo.Linear(
        4,
        8,
        seed=0
    )

    x = eo.zeros(16, 4)

    output = layer(x)

    assert output.shape == (16, 8)

def test_sequential_model_shape():
    model = eo.Sequential(
        eo.Linear(4, 8, seed=0),
        eo.ReLU(),
        eo.Linear(8, 2, seed=1)
    )

    output = model(eo.zeros(16, 4))

    assert output.shape == (16, 2)

Optimizers and losses must produce mathematically meaningful results.

• Parameters update after a step.
• Zero gradients are handled.
• Optimizer state behaves correctly.
• Invalid hyperparameters are rejected.
• Updates remain numerically stable.

• Expected output shape.
• Known reference values.
• Gradient correctness.
• Target compatibility.
• Numerical stability.

Use simple functions with known solutions.

For example:

[ L(w)=(w-3)^2 ]

The optimum is:

[ w^*=3 ]

An optimizer test can verify that repeated updates reduce this objective under a suitable learning rate.

The data pipeline should preserve sample correctness and batch integrity.

import numpy as np
import everyo as eo

def test_dataloader_batch_count():
    features = np.zeros(
        (10, 2),
        dtype=np.float32
    )

    targets = np.zeros(
        10,
        dtype=np.int64
    )

    dataset = eo.ArrayDataset(
        features,
        targets
    )

    loader = eo.DataLoader(
        dataset,
        batch_size=4
    )

    assert len(loader) == 3

• Empty datasets.
• Invalid batch size.
• Incomplete final batch.
• Shuffling.
• Feature-target alignment.
• Reproducibility.
• Custom dataset collation.

Integration tests verify complete workflows.

Dataset
   |
   v
DataLoader
   |
   v
Neural Network
   |
   v
Loss Function
   |
   v
Autograd
   |
   v
Optimizer
   |
   v
Updated Model

• Complete training loop.
• Validation workflow.
• Callback execution.
• Checkpoint restoration.
• Prediction after training.
• Training history recording.

Integration tests should verify not only that execution completes, but that meaningful invariants hold.

For example, an appropriately configured model should demonstrate learning on a simple deterministic problem.

EveryO includes CPU and optional accelerator functionality.

The CPU path should remain testable without CUDA hardware.

CUDA-specific tests should:

• Check availability.
• Skip gracefully when unavailable.
• Verify supported operations.
• Compare numerical results with CPU.
• Test error handling.

from everyo.core.device import device

def test_cpu_device():
    resolved = device("cpu")

    assert resolved.type == "cpu"

The repository defines pytest markers:

cuda
tensorflow
slow

Example:

import pytest

@pytest.mark.cuda
def test_cuda_specific_operation():
    ...

Markers classify tests; they do not automatically implement environment checks or skipping.

Model serialization must preserve architecture and numerical behavior.

• Save a model.
• Load the archive.
• Compare parameters.
• Compare predictions.
• Verify metadata.
• Reject malformed files.

The repository includes:

tests/test_onnx.py
tests/test_onnx_trace.py

EveryO Model
      |
      v
   ONNX Export
      |
      v
  ONNX Runtime
      |
      v
Compare Predictions
      |
      v
Numerical Verification

np.testing.assert_allclose(
    everyo_output,
    onnx_output,
    rtol=1e-5,
    atol=1e-6
)

Choose tolerances appropriate to the operation and backend.

Performance tests detect unintended slowdowns.

• Forward-pass latency.
• Backward-pass latency.
• Training throughput.
• Memory consumption.
• DataLoader throughput.
• Serialization time.

[ R= \frac{T_{new}-T_{baseline}} {T_{baseline}}\times100 ]

where (T) is elapsed time.

1. Record a stable baseline.
2. Use equivalent inputs.
3. Warm up the workload.
4. Run repeated measurements.
5. Compare distributions.
6. Investigate meaningful regressions.

Performance results from shared CI runners can be noisy.

A dedicated benchmark environment is preferable for enforcing strict performance thresholds.

Code coverage indicates which code was exercised during testing.

python -m pip install pytest-cov

python -m pytest \
  --cov=everyo \
  --cov-report=term-missing

python -m pytest \
  --cov=everyo \
  --cov-report=html

Open:

htmlcov/index.html

High line coverage does not guarantee mathematical correctness.

Combine coverage with meaningful assertions, edge cases, numerical gradient checks, and integration tests.

EveryO uses Ruff for linting and formatting checks.

ruff check .

ruff format --check .

ruff format .

The repository declares:

[tool.ruff]
line-length = 100
target-version = "py39"

The development dependency pins Ruff to a specific version to reduce formatter drift between local development and CI.

• Resolve lint violations before submitting a PR.
• Avoid unrelated formatting changes.
• Keep imports organized.
• Prefer readable and testable code.

EveryO uses GitHub Actions for automated validation.

.github/workflows/
├── tests.yml
└── cuda-build.yml

The inspected tests.yml runs on:

• Pushes.
• Pull requests.
• Manual workflow dispatch.

The inspected workflow tests:

Push / Pull Request
        |
        v
   GitHub Actions
        |
        +-- Unit Tests
        |
        +-- Integration Tests
        |
        +-- Doctests
        |
        +-- Ruff Lint
        |
        +-- Formatting
        |
        +-- TensorFlow Tests
        |
        +-- ONNX Tests
        |
        +-- Examples
        |
        +-- Package Build

View the current execution results:

EveryO GitHub Actions

Security checks should complement functional tests.

• Dependency vulnerability scanning.
• Secret detection.
• Static security analysis.
• Safe deserialization testing.
• Input validation.
• Least-privilege CI permissions.

A proposed local audit command is:

python -m pip install pip-audit

python -m pip_audit

This requires the external pip-audit package.

These are recommendations; they are not claims that every check is already configured in EveryO.

Documentation examples must remain consistent with the source code.

The existing CI workflow runs:

python -m pytest \
  --doctest-modules everyo -q \
  --ignore=everyo/serialization/onnx_export.py \
  --ignore=everyo/serialization/onnx_trace.py

The ONNX exporters have dedicated doctest execution in the ONNX job.

• Use valid public APIs.
• Verify imports.
• Include correct expected outputs.
• Explain optional dependencies.
• Avoid fabricated benchmark values.
• Distinguish conceptual examples from executable code.

• Page title is accurate.
• Code examples are valid.
• API signatures match the source.
• Internal links are correct.
• Version requirements are documented.
• Mathematical formulas are verified.
• Limitations are explained.

Cross-platform testing helps identify operating-system and runtime differences.

• Linux.
• Windows.
• macOS.

• Filesystem path differences.
• Environment activation.
• Dependency installation.
• Numerical library behavior.
• Native extension compilation.
• Multiprocessing behavior.

Use platform-independent paths:

from pathlib import Path

output = Path("artifacts") / "model.evo"

Avoid hard-coded platform-specific separators when unnecessary.

Reproducible tests produce consistent outcomes under controlled conditions.

EveryO's tests/conftest.py includes a seeded NumPy random generator fixture.

Example:

import numpy as np
import pytest

@pytest.fixture
def rng():
    return np.random.default_rng(1234)

• Explicit random seeds.
• Deterministic reference inputs.
• Stable test tolerances.
• Documented dependency versions.
• Controlled environment assumptions.

Do not depend unnecessarily on:

• Exact execution timing.
• Unstable sample ordering.
• External network services.
• Machine-specific paths.
• Uncontrolled random values.

The following gates are recommended for a formal EveryO release process.

Candidate Release
       |
       v
Functional Tests
       |
       v
Numerical Validation
       |
       v
Compatibility Tests
       |
       v
Security Review
       |
       v
Package Verification
       |
       v
Maintainer Approval
       |
       v
Release Publication

These are recommended release criteria. They become enforceable only when adopted through repository governance, CI configuration, and branch protection or equivalent controls.

python -m pip install -e ".[dev]"

Before submitting a pull request:

• Understand the affected subsystem.
• Add tests for new behavior.
• Add regression tests for bug fixes.
• Verify numerical correctness where relevant.
• Run the relevant test files.
• Run the full test suite.
• Run Ruff linting.
• Run formatting checks.
• Update documentation.
• Review compatibility impacts.
• Confirm no credentials are committed.
• Describe validation in the PR.

python -m pytest -q

ruff check .

ruff format --check .

python -m compileall -q everyo

python -m build

The final command requires the build package, which can be installed with:

python -m pip install build

Continue with EveryO's development and contributor documentation.

Wiki links become active after their corresponding pages are created.