Quality Assurance & Testing Strategy¶

This document defines the quality assurance (QA) strategy for WhiteLotusAI, covering application functionality, AI orchestration, external integrations, security controls, and deployment readiness.

The objective is to establish a repeatable, evidence-based testing process that detects defects early, protects connected resources, and provides confidence in the behavior of each release.

1. Objectives¶

The testing strategy aims to:

• Verify correctness: Confirm that application behavior meets documented requirements.
• Protect security boundaries: Validate authentication, authorization, tenant isolation, and data access restrictions.
• Validate AI workflows: Test intent routing, tool selection, argument validation, and model failure handling.
• Protect connected resources: Prevent unintended changes to data in external services.
• Improve reliability: Detect regressions, integration failures, and configuration errors before release.
• Establish release confidence: Require reproducible test evidence and documented acceptance decisions.

2. Testing principles¶

3. Test levels and scope¶

The following levels define the expected testing coverage. They are a strategy, not a claim that every category is already automated in the repository.

3.1 Recommended test pyramid¶

Use a test pyramid to balance fast feedback with realistic system validation.

flowchart TB
    E2E["End-to-end tests<br/>Critical user journeys"]
    API["API and service integration tests<br/>Contracts and boundaries"]
    UNIT["Unit tests<br/>Business rules and validators"]

    E2E --> API
    API --> UNIT

    classDef e2e fill:#E8DDFB,stroke:#805AD5,color:#30204F,stroke-width:1.5px
    classDef api fill:#DCEBFA,stroke:#3975B7,color:#17365D,stroke-width:1.5px
    classDef unit fill:#D9F2E6,stroke:#23835B,color:#174B37,stroke-width:1.5px

    class E2E e2e
    class API api
    class UNIT unit

4. AI orchestration testing¶

AI workflows require testing beyond conventional request-response validation. WhiteLotusAI must distinguish deterministic application rules from probabilistic model behavior.

4.1 Required AI test scenarios¶

4.2 Deterministic and model-based tests¶

Use deterministic tests for application rules and controlled evaluation for model-dependent behavior.

5. Integration testing and external services¶

WhiteLotusAI integrates with external services that may contain confidential business information. Integration tests must validate both provider behavior and the application's authorization boundaries.

5.1 Integration test requirements¶

• Mock external providers for routine, deterministic test execution.
• Use isolated test accounts and synthetic data for approved live integration tests.
• Validate token handling, expired credentials, refresh failures, rate limits, and provider errors.
• Verify that user identity and authorization context are preserved across service boundaries.
• Test message-queue correlation, timeout handling, and failed request/reply interactions where applicable.
• Ensure logs and error responses do not expose access tokens, internal secrets, or confidential payloads.
• Verify cleanup of temporary resources created by test execution.

5.2 Read-only integration safety¶

The initial integration scope is read-only. This restriction must be enforced independently of the model's intent or generated output.

6. Security testing¶

Security tests must target both individual services and the boundaries between them.

7. Test data and environment management¶

7.1 Test data requirements¶

• Prefer synthetic or appropriately anonymized datasets.
• Keep fixtures small, deterministic, and version-controlled where appropriate.
• Never commit access tokens, passwords, private keys, or production credentials.
• Avoid copying production records into local development environments.
• Reset mutable test state between test cases where necessary.
• Define retention and cleanup requirements for generated test artifacts.

7.2 Environment separation¶

8. Test execution and reporting¶

Tests should be runnable through documented, repeatable commands. The commands below are examples for a Python service using pytest; adapt them to the repository's actual test layout and installed dependencies.

# Run the test suite
python -m pytest

# Run a specific test directory
python -m pytest tests/

# Run tests with verbose output
python -m pytest -v

# Requires pytest-cov to be installed
python -m pytest --cov --cov-report=term-missing

# Generate a machine-readable coverage report
python -m pytest --cov --cov-report=xml:coverage.xml

# Replace the path with the relevant test module
python -m pytest tests/test_authorization.py -v

# Stop after the first failure when debugging
python -m pytest tests/ -x

8.1 Required test reporting¶

Test reports should capture:

• Commit or release identifier.
• Environment and relevant dependency versions.
• Test command and scope.
• Passed, failed, skipped, and blocked test counts.
• Coverage results when generated by a reproducible process.
• Security findings and unresolved high-risk defects.
• Relevant logs or artifacts, with sensitive values removed.
• Final acceptance decision and any approved exceptions.

Coverage percentages must be generated from actual test execution. Coverage alone does not establish correctness, completeness, or security.

9. Defect lifecycle¶

flowchart TD
    A["Record defect"] --> B["Assess severity and impact"]
    B --> C["Reproduce and investigate"]
    C --> D["Implement corrective change"]
    D --> E["Add regression test"]
    E --> F["Run relevant test suites"]
    F --> G{"Validation passed?"}
    G -- "No" --> C
    G -- "Yes" --> H["Review and document evidence"]
    H --> I["Close defect or approve release"]

    classDef work fill:#DCEBFA,stroke:#3975B7,color:#17365D,stroke-width:1.3px
    classDef decision fill:#FCE8CF,stroke:#B7791F,color:#704214,stroke-width:1.3px
    classDef done fill:#D9F2E6,stroke:#23835B,color:#174B37,stroke-width:1.3px

    class A,B,C,D,E,F,H work
    class G decision
    class I done

Every defect should follow this process:

1. Record the defect, impact, and reproduction steps.
2. Assess severity, affected components, and security implications.
3. Identify the root cause using source inspection and diagnostic evidence.
4. Implement the smallest appropriate corrective change.
5. Add or update a regression test.
6. Run the relevant tests and assess related failure modes.
7. Review the change and its test evidence.
8. Document the outcome and close the defect only when acceptance criteria are met.

10. Release acceptance criteria¶

A release candidate should be evaluated against the following checklist. Items should be marked complete only when supporting evidence is available.

• Required unit, API, and integration tests pass.
• Critical user workflows have been validated.
• Security-sensitive authorization and isolation tests pass.
• Read-only integration restrictions have been verified.
• AI failure handling and untrusted-content scenarios have been tested.
• Database migrations and configuration have been validated.
• Required dependency and container security checks have been reviewed.
• Observability and error reporting are sufficient for investigation.
• Backup and rollback procedures have been reviewed and tested where required.
• Known defects and residual risks have documented dispositions.
• Test results and the release decision are recorded.

11. Quality metrics¶

Use metrics to identify trends and areas for improvement rather than as standalone indicators of product quality.

Where numerical targets are required, establish explicit baselines and approved thresholds. Do not invent targets or report unmeasured values.

12. Continuous improvement¶

Review the strategy as WhiteLotusAI evolves. Update test coverage and acceptance criteria when introducing new services, integrations, AI workflows, security controls, or deployment mechanisms.

Use test failures, production incidents, security reviews, and defect trends to identify missing coverage. Prioritize improvements according to user impact, likelihood of failure, and potential security consequences.

Related documentation

• API endpoints and contracts
• API error handling
• Security overview
• Threat model
• Read-only integration security policy
• Deployment guide