Testing Strategy

• Testing Strategy
• 1. Introduction
  • 1.1 Overview
  • 1.2 Scope
    • 1.2.1 Constraints
    • 1.2.2 Assumptions
    • 1.2.3 In Scope
    • 1.2.4 Out of Scope
  • 1.3 Objectives
  • 1.4 Executive Summary
• 2. Test Strategy
  • 2.1 Test Approach
    • 2.1.1 Test Coverage
    • 2.1.2 Test Automation
    • 2.1.3 Regression Testing
  • 2.2 Test Types
    • 2.2.1 Integration Testing
    • 2.2.2 System Testing
    • 2.2.3 Manual & Exploratory Testing
  • 2.3 Test Tools
  • 2.4 Test Environment
  • 2.5 Risks and Mitigation
• 3. Test Plan
  • 3.1 Test Schedule
  • 3.2 Responsibilities and Roles
  • 3.3 Test Cases
  • 3.4 Metrics and Reporting
• 4. Deliverables (To be determined)
• 5. Dependencies
• 6. Appendix
  • 6.1 Acronyms and Abbreviations
  • 6.2 References

---

1. Introduction

1.1 Overview

This document defines the test strategy for the pyicub Python library. The aim is to provide robust, reliable, and maintainable Python interfaces for robot development, with a testing strategy that ensures high quality and repeatability.

1.2 Scope

1.2.1 Constraints

• Hardware Independence: All automated tests must run in simulation (Gazebo) to avoid dependency on physical iCub robots.
• Python Support: All tests must pass on all supported Python versions (e.g., 3.8+).
• CI/CD Integration: The testing strategy must be compatible with continuous integration systems (e.g., GitHub Actions).
• Open Source Compliance: Testing tools and scripts should use open-source licenses.
• Coverage Requirement: A minimum of 90% code coverage is required for core modules.

1.2.2 Assumptions

• The codebase will be actively maintained and extended by multiple developers.
• All major features are testable within a simulated environment.
• Test data and simulation scenarios are representative of typical usage.

1.2.3 In Scope

• Integration and system (end-to-end) tests for all public modules.
• Static analysis, linting, and type checks.
• Manual and exploratory testing for critical workflows.
• Automated test execution in both local and CI environments.

1.2.4 Out of Scope

• Non-simulated (physical lab) acceptance tests.

1.3 Objectives

• Validate correctness, stability, and maintainability of the pyicub library.
• Ensure high code coverage and early defect detection.
• Enable seamless automated testing both locally and in CI.
• Document testing processes, outcomes, and guidelines.

1.4 Executive Summary

This strategy outlines the multi-layered approach to testing for pyicub. It includes automated integration and system testing (primarily with pytest), static code analysis, and manual review, all conducted in a reproducible simulation environment. The strategy ensures robust releases, high code quality, and readiness for real-world robotics applications.

---

2. Test Strategy

2.1 Test Approach

2.1.1 Test Coverage

• Modules: Integration tests must cover all core modules, interfaces, and helper functions to ensure interaction and functionality.
• Interfaces: Robot hardware interfaces, communications, and workflows validated in simulation(Gazebo).
• Error Handling: All exception and edge-case paths must be tested.
• Performance: Basic responsiveness tests in simulation.

2.1.2 Test Automation

• pytest is the standard framework for all automated Python testing, providing test discovery, fixtures, and parameterization.
• All tests are run both locally and in CI pipelines.
• Coverage is measured by pytest-cov.
• Regression tests are retained and run for each release and PR.

2.1.3 Regression Testing

• All resolved bugs must have corresponding regression tests.
• Regression suite is executed for every pull request and major code change.

2.2 Test Types

2.2.1 Integration Testing

• Goal: Validate interactions between modules (e.g., command pipelines, event handling).
• Scope: Subsystem interactions, robot interface with Gazebo/YARP layers.

2.2.2 System Testing

• Goal: Validate end-to-end workflows in a full simulation.
• Scope: Full startup, motion sequences, sensor feedback, shutdown.

2.2.3 Manual & Exploratory Testing

• Goal: Identify usability issues and edge cases not easily automated.
• Scope: New features, complex behaviors, user experience, and real robot (if available).
• Practice: Conducted by developers during feature completion or release review.

2.3 Test Tools

• pytest: Main Python test runner.
• pytest-cov: Coverage measurement.
• mypy: Static type checking.
• flake8: Linting and code style.
• Gazebo: Robot simulation backend.
• YARP: Middleware for robot modules.
• GitHub Actions: CI/CD automation.

2.4 Test Environment

• Development: Local machine with Dockerized simulation and Python environment.
• Simulation: Gazebo launched in either interactive (GUI) or headless mode.
• CI: Headless, automated Docker containers executing the full suite.
• Test Data: Synthetic and scenario-based, versioned with the codebase.

2.5 Risks and Mitigation

• Simulation Drift: Regularly validate simulation environment matches real robot capabilities.
• Coverage Gaps: Enforce code coverage metrics and peer review for test completeness.
• CI Failures: Automate environment setup; use stable, versioned Docker images.
• Test Flakiness: Stabilize flaky tests(so inconsistent outcome of tests) by isolating side effects.

---

3. Test Plan

3.1 Test Schedule

• Continuous: Testing is triggered on each code push, pull request, and scheduled in CI (to define the frequency of the trigger).

3.2 Responsibilities and Roles

• Developers: Write and maintain tests for their code, review test failures.
• Maintainers: Oversee CI status, coverage reports, and overall quality.

3.3 Test Cases

• Test cases are maintained alongside code in the tests/ directory.
• Complex scenarios will be (most likely) described in markdown or reStructuredText files within tests/docs/.

3.4 Metrics and Reporting

• Code Coverage: % of lines and branches tested (minimum 90%).
• Test Pass Rate: Percentage of passing tests per CI run.
• Defect Density: Number of bugs reported per release.
• Resolution Time: Mean time to resolve failed CI runs or critical bugs.
• Manual Review Logs: Outcomes and findings of exploratory/manual sessions.

---

4. Deliverables (To be determined)

• Testing Strategy Document: This document, versioned with the repository.
• Test Suite: All automated and manual test cases/scripts.
• Coverage & Lint Reports: Generated on every CI run.
• Final Test Report: Compiled for major releases, summarizing results, coverage, and known issues.

---

5. Dependencies

• Python 3.8+
• pytest, pytest-cov, mypy, flake8
• Gazebo, YARP
• Docker (for simulation and CI)
• GitHub Actions (for CI/CD)

---

6. Appendix

6.1 Acronyms and Abbreviations

Acronym	Definition
API	Application Programming Interface
CI	Continuous Integration
CD	Continuous Deployment
YARP	Yet Another Robot Platform
QA	Quality Assurance
PEP8	Python Enhancement Proposal 8 (style guide)
IDE	Integrated Development Environment

6.2 References

• pytest
• Gazebo
• YARP
• GitHub Actions
• mypy
• flake8

This document will be reviewed and updated as the project evolves, to ensure ongoing quality and relevance.