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Testing Guide

This document explains the testing philosophy, architecture, workflows, strategies, and long-term validation systems used in Aniwa.

Testing is one of the most important engineering systems in the project because it ensures:

  • reliability
  • stability
  • scalability
  • contributor confidence
  • release quality

Aniwa treats testing as:

core infrastructure

not an optional development step.

Purpose of Testing

Testing ensures that:

  • features work correctly
  • regressions are prevented
  • releases remain stable
  • contributors can collaborate safely

Testing Philosophy

Aniwa testing prioritizes:

Principle Purpose
correctness features behave properly
reproducibility predictable outcomes
maintainability sustainable engineering
scalability long-term project growth
confidence safer contributions

Core Philosophy

Aniwa aims for:

high-confidence engineering

through:

predictable automated validation

Why Testing Matters

Without testing:

  • regressions increase
  • contributors break existing systems
  • releases become unstable
  • debugging becomes expensive

Current Testing Framework

Aniwa currently uses:

pytest

Why pytest Was Chosen

pytest provides:

  • clean syntax
  • strong ecosystem
  • scalability
  • excellent developer experience

Test Directory Structure

Tests are stored in:

tests/

Example Test Structure

Example:

tests/
├── test_cli.py
├── test_readers.py
├── test_reports.py
├── test_config.py
├── test_insights.py

Test Organization Philosophy

Tests should mirror:

the architecture of the project

Why Mirrored Structures Matter

Mirrored structures improve:

  • discoverability
  • maintainability
  • contributor onboarding

Core Testing Categories

Aniwa currently focuses on several testing layers.

Unit Tests

Unit tests validate:

individual functions and modules

Integration Tests

Integration tests validate:

systems working together

CLI Tests

CLI tests validate:

  • commands
  • options
  • argument handling
  • UX behavior

Reader Tests

Reader tests validate:

  • dataset loading
  • supported formats
  • malformed files

Report Tests

Report tests validate:

  • rendering
  • export generation
  • output structure

Config Tests

Config tests validate:

  • YAML loading
  • TOML loading
  • JSON loading
  • override priority

Insight Tests

Insight tests validate:

  • rule triggering
  • severity correctness
  • false positives

Metadata Tests

Metadata tests validate:

  • runtime tracking
  • report metadata
  • command reconstruction

Current Testing Flow

Typical development flow:

implement
→ test
→ validate
→ refactor
→ release

Running Tests

Run all tests using:

pytest

Running Verbose Tests

Example:

pytest -v

Running Specific Tests

Example:

pytest tests/test_cli.py

Running Individual Test Functions

Example:

pytest tests/test_cli.py::test_help

Why Granular Testing Matters

Granular testing improves:

  • debugging speed
  • iteration speed
  • contributor productivity

Testing Philosophy

Tests should be:

Principle Meaning
isolated independent
deterministic repeatable
readable understandable
maintainable sustainable

Deterministic Testing

Tests should always produce:

predictable outcomes

Why Determinism Matters

Non-deterministic tests create:

  • flaky CI
  • contributor confusion
  • unreliable validation

Good Test Naming

Good test names explain behavior.

Examples

Examples:

test_read_csv()
test_invalid_config()
test_render_html_report()

Bad Test Names

Avoid vague names like:

test_stuff()
test_feature()

Test Function Philosophy

Each test should validate:

one clear behavior

Why Small Tests Matter

Small tests improve:

  • readability
  • debugging
  • maintainability

Assertion Philosophy

Assertions should remain:

explicit and readable

Good Assertion Example

Example:

assert df.height == 5

Avoid Weak Assertions

Avoid:

assert result

when more specific validation is possible.

Edge Case Philosophy

Always test:

  • invalid inputs
  • empty datasets
  • malformed files
  • extreme values

Why Edge Cases Matter

Most real-world failures happen at:

system boundaries

Example Edge Cases

Examples:

Case
empty CSV
corrupted JSON
unsupported extension
invalid config values

Reader Testing Philosophy

Readers should validate:

  • correct parsing
  • format handling
  • conversion to Polars

Example Reader Test

Example:

def test_read_csv():
    df = read_dataset("examples/customers.csv")

    assert df.height > 0

Config Testing Philosophy

Config systems must validate:

  • loading
  • precedence
  • invalid values
  • graceful failure

Config Priority Testing

Verify:

CLI > config > defaults

Example Config Test

Example:

def test_cli_overrides_config():

Report Testing Philosophy

Reports should validate:

  • rendering success
  • file creation
  • structure correctness

Example Report Test

Example:

def test_render_json_report():

HTML Report Validation

HTML reports should validate:

  • rendering
  • templates
  • chart injection

PDF Report Validation

PDF systems should validate:

  • successful generation
  • layout correctness
  • export integrity

Markdown Report Validation

Markdown reports should validate:

  • formatting
  • readability
  • structure

CLI Testing Philosophy

CLI tests should validate:

  • help output
  • options
  • invalid arguments
  • execution flow

Example CLI Test

Example:

def test_help():

Metadata Testing Philosophy

Metadata systems should validate:

  • generated timestamps
  • version info
  • execution metadata

Template Testing Philosophy

Templates should validate:

  • rendering
  • responsiveness
  • empty states

Insight Testing Philosophy

Insight systems should validate:

  • proper triggering
  • severity assignment
  • noise reduction

False Positive Philosophy

Insight systems should avoid:

excessive unnecessary warnings

Performance Testing Philosophy

Performance systems should eventually validate:

  • execution speed
  • memory usage
  • scalability

Why Performance Testing Matters

Aniwa is designed for:

large-scale data profiling

Future Performance Benchmarks

Future benchmarks may track:

Metric
runtime
memory usage
large dataset handling

Future Scalability Tests

Potential future tests:

  • distributed execution
  • chunked processing
  • parallel profiling

Mocking Philosophy

Mocking should be used:

carefully and minimally

Why Excessive Mocking Is Dangerous

Too much mocking can hide:

  • real integration failures
  • architectural problems

Integration Testing Philosophy

Integration tests ensure:

subsystems work together correctly

Example Integration Flow

Example:

reader
→ profiler
→ report

Manual Testing Philosophy

Some features still require:

human validation

Examples

Examples:

Area
visual reports
PDF layouts
CLI UX
chart rendering

Recommended Manual Checks

Before releases:

  • inspect HTML reports
  • verify PDFs
  • test configs manually
  • validate examples

Example Manual Validation

Example:

aniwa examples/customers.csv --report html

Continuous Integration Vision

Future systems may automate:

  • testing
  • formatting
  • linting
  • release validation

Potential Future Tooling

Potential future systems:

Tool
GitHub Actions
CI pipelines
automated release testing

Future Coverage Systems

Future systems may include:

coverage tracking

Why Coverage Matters

Coverage helps identify:

  • untested systems
  • weak validation areas

Recommended Future Tools

Potential tools:

Tool
coverage.py
pytest-cov

Failure Philosophy

When tests fail:

investigate first, patch second

Debugging Workflow

Recommended process:

reproduce
→ isolate
→ inspect
→ fix
→ retest

Avoid Test Suppression

Never ignore failing tests without understanding:

  • root cause
  • architectural impact

Regression Philosophy

Every major bug should eventually receive:

a regression test

Why Regression Tests Matter

Regression tests prevent:

old bugs from returning

Contributor Testing Expectations

Contributors should:

  • add tests for new features
  • update broken tests
  • validate edge cases

Pull Request Requirements

Good PRs should include:

  • tests
  • validation
  • documentation updates

Testing Anti-Patterns

Avoid:

Anti-Pattern
flaky tests
vague assertions
giant test functions
silent failures
skipped edge cases

Long-Term Testing Vision

Aniwa’s testing infrastructure is evolving toward:

enterprise-grade validation systems

Future Enterprise Testing

Potential future capabilities:

Capability
distributed testing
benchmark automation
security testing
cloud validation

Strategic Importance

Testing is not merely:

a development task

It is:

a foundational engineering system

that protects the long-term quality of the project.

Final Philosophy

Testing exists to ensure that:

Aniwa remains reliable, scalable, maintainable, and trustworthy as it evolves

Related Documentation

Continue with:

  • developer-guide/local-development.md
  • developer-guide/code-style.md
  • architecture/system-design.md
  • CONTRIBUTING.md