Skip to content

STP-Software-Test-Plan.md

Latest commit

 

History

History
644 lines (482 loc) · 20.1 KB

STP-Software-Test-Plan.md

File metadata and controls

644 lines (482 loc) · 20.1 KB

Software Test Plan (STP)

Python-SLAM Real-Time Visual SLAM System

Document Number: STP-PYTHON-SLAM-001 Version: 1.0 Date: October 2, 2025 Classification: Unclassified Prepared by: Python-SLAM Test Team Approved by: [Test Manager]

Document Control

Version Date Author Description of Changes
1.0 2025-10-02 Test Team Initial test plan

1. Introduction

1.1 Purpose

This Software Test Plan (STP) defines the comprehensive testing strategy, approach, and procedures for the Python-SLAM real-time visual SLAM system. The plan ensures thorough verification of all functional and non-functional requirements while maintaining traceability to the Software Requirements Document (SRD).

1.2 Scope

Testing Scope:

  • Functional Testing: All SLAM core functions, GUI components, and integrations
  • Performance Testing: Real-time processing, GPU acceleration, embedded optimization
  • Integration Testing: ROS2 integration, multi-component interactions
  • System Testing: End-to-end SLAM workflows and user scenarios
  • Regression Testing: Automated test suite for continuous integration

Out of Scope:

  • Hardware driver testing (assumes working camera hardware)
  • Operating system testing (relies on platform APIs)
  • Third-party library testing (OpenCV, PyQt, etc.)

1.3 Test Objectives

Objective Success Criteria Priority
Functional Verification 100% requirements coverage, all tests pass High
Performance Validation Meet all performance targets (30 FPS, <100ms latency) High
Integration Verification All interfaces work correctly with external systems High
Reliability Validation 24+ hour continuous operation without failure Medium
Usability Verification User tasks completed within time/error limits Medium

2. Test Strategy and Approach

2.1 Testing Levels

graph TB
    subgraph "Unit Testing"
        UT1[Individual Functions]
        UT2[Class Methods]
        UT3[Algorithm Components]
    end

    subgraph "Integration Testing"
        IT1[Module Integration]
        IT2[Subsystem Integration]
        IT3[External System Integration]
    end

    subgraph "System Testing"
        ST1[End-to-End Workflows]
        ST2[Performance Testing]
        ST3[Stress Testing]
    end

    subgraph "Acceptance Testing"
        AT1[User Scenario Testing]
        AT2[Benchmark Validation]
        AT3[Production Readiness]
    end

    UT1 --> IT1
    UT2 --> IT1
    UT3 --> IT1
    IT1 --> ST1
    IT2 --> ST2
    IT3 --> ST3
    ST1 --> AT1
    ST2 --> AT2
    ST3 --> AT3
Loading

2.2 Test Types and Methods

Test Type Method Coverage Automation Level
Unit Tests White-box testing of individual functions 90%+ code coverage Fully automated
Integration Tests Black-box testing of component interfaces All interface contracts Fully automated
Performance Tests Timing and resource measurement All performance requirements Semi-automated
System Tests End-to-end scenario testing All user workflows Semi-automated
Regression Tests Automated test suite execution Changed/affected code Fully automated
Stress Tests Resource exhaustion and edge cases System limits and failure modes Manual/automated
Usability Tests User task completion and satisfaction GUI and API usability Manual

2.3 Test Environment Strategy

2.3.1 Test Environment Matrix

Environment Purpose Hardware OS Automation
Development Unit/Integration testing Developer workstations Linux/Windows/macOS CI/CD
Performance Performance/stress testing High-end desktop (i7, 32GB, RTX 4070) Linux Semi-automated
Embedded Embedded optimization testing Raspberry Pi 4, Jetson Nano Linux ARM Manual
Integration ROS2/external system testing Multi-node setup Ubuntu 22.04 Automated
Production Final validation testing Target deployment hardware Production OS Manual

2.3.2 Test Data Management

Synthetic Test Data:

  • Generated stereo image sequences with known ground truth
  • Controlled lighting and motion patterns
  • Configurable noise and distortion parameters

Real Dataset Integration:

  • TUM RGB-D Dataset for indoor SLAM evaluation
  • KITTI Dataset for outdoor automotive scenarios
  • EuRoC MAV Dataset for aerial vehicle SLAM
  • Custom recorded sequences for specific test cases

3. Test Cases and Requirements Mapping

3.1 Functional Requirements Testing

3.1.1 REQ-F-001: Real-Time SLAM Processing

Test Case TC-F-001-01: Basic SLAM Pipeline Processing

  • Objective: Verify SLAM pipeline processes stereo input at 30+ FPS
  • Prerequisites: Calibrated stereo camera, test image sequence
  • Test Steps:
    1. Initialize SLAM system with default configuration
    2. Feed stereo image sequence at 30 FPS
    3. Monitor processing frame rate and latency
    4. Verify pose estimates are generated for each frame
  • Expected Results:
    • Frame rate ≥30 FPS achieved
    • Latency <100ms per frame
    • Valid pose estimates for >95% of frames
  • Pass Criteria: All expected results met
  • Requirements Verified: REQ-F-001, REQ-NF-P-001

Test Case TC-F-001-02: SLAM Accuracy Validation

  • Objective: Verify SLAM accuracy meets specification (<1cm RMS)
  • Prerequisites: TUM dataset with ground truth trajectory
  • Test Steps:
    1. Process complete TUM sequence through SLAM pipeline
    2. Compare estimated trajectory with ground truth
    3. Calculate Absolute Trajectory Error (ATE)
    4. Calculate Relative Pose Error (RPE)
  • Expected Results:
    • ATE RMSE <1cm for standard sequences
    • RPE translation error <1% of traveled distance
    • No catastrophic tracking failures
  • Pass Criteria: Accuracy targets met for 80% of test sequences
  • Requirements Verified: REQ-F-001, REQ-F-006

3.1.2 REQ-F-002: Feature Extraction and Tracking

Test Case TC-F-002-01: ORB Feature Extraction Performance

  • Objective: Verify ORB extracts 500+ features from 640x480 images
  • Prerequisites: Standard test image set (indoor/outdoor/varying lighting)
  • Test Steps:
    1. Configure SLAM for ORB feature detection
    2. Process test images through feature extraction
    3. Count extracted features per image
    4. Measure extraction time
  • Expected Results:
    • 500+ features extracted from well-textured images
    • <10ms processing time per image
    • Consistent detection across lighting conditions
  • Pass Criteria: Feature count and timing targets met for 90% of test images
  • Requirements Verified: REQ-F-002

Test Case TC-F-002-02: Multi-Algorithm Feature Support

  • Objective: Verify runtime switching between ORB, SIFT, SURF detectors
  • Prerequisites: Test images, algorithm implementations
  • Test Steps:
    1. Initialize with ORB detector
    2. Process several frames
    3. Switch to SIFT detector during processing
    4. Switch to SURF detector
    5. Verify tracking continuity across switches
  • Expected Results:
    • Successful algorithm switching without crashes
    • Tracking maintained across algorithm changes
    • Performance characteristics as expected per algorithm
  • Pass Criteria: All algorithm switches successful, tracking maintained
  • Requirements Verified: REQ-F-002

3.1.3 REQ-F-007: GPU Acceleration Support

Test Case TC-F-007-01: Multi-Vendor GPU Detection

  • Objective: Verify system detects and utilizes NVIDIA, AMD, Apple GPUs
  • Prerequisites: Test systems with different GPU vendors
  • Test Steps:
    1. Run GPU detection on NVIDIA system (CUDA)
    2. Run GPU detection on AMD system (ROCm)
    3. Run GPU detection on Apple system (Metal)
    4. Verify automatic backend selection
  • Expected Results:
    • Correct GPU vendor detection
    • Appropriate backend initialization
    • Fallback to CPU when GPU unavailable
  • Pass Criteria: Correct detection and initialization on all tested platforms
  • Requirements Verified: REQ-F-007

Test Case TC-F-007-02: GPU Acceleration Performance

  • Objective: Verify >3x speedup for feature extraction on GPU vs CPU
  • Prerequisites: System with compatible GPU, benchmarking tools
  • Test Steps:
    1. Benchmark feature extraction on CPU-only mode
    2. Benchmark feature extraction with GPU acceleration
    3. Compare processing times
    4. Verify result consistency between CPU and GPU
  • Expected Results:
    • GPU processing >3x faster than CPU
    • Identical or equivalent results from both methods
    • GPU memory usage <4GB during operation
  • Pass Criteria: Performance improvement targets met, results consistent
  • Requirements Verified: REQ-F-007, REQ-NF-P-002

3.2 Non-Functional Requirements Testing

3.2.1 REQ-NF-P-001: Real-Time Processing Performance

Test Case TC-NF-P-001-01: Sustained Performance Testing

  • Objective: Verify sustained 30 FPS performance over extended operation
  • Prerequisites: Long test sequence (30+ minutes), performance monitoring
  • Test Steps:
    1. Initialize SLAM with performance monitoring enabled
    2. Process continuous stereo feed for 30 minutes
    3. Monitor frame rate, latency, and resource usage
    4. Record performance degradation over time
  • Expected Results:
    • Average frame rate ≥30 FPS throughout test
    • 95th percentile latency <150ms
    • No significant performance degradation over time
  • Pass Criteria: Performance targets maintained for duration of test
  • Requirements Verified: REQ-NF-P-001

3.2.2 REQ-NF-R-001: System Stability

Test Case TC-NF-R-001-01: 24-Hour Stability Testing

  • Objective: Verify system operates continuously for 24+ hours without failure
  • Prerequisites: Automated test harness, continuous data feed
  • Test Steps:
    1. Start SLAM system with automated monitoring
    2. Feed continuous synthetic or recorded data
    3. Monitor for crashes, memory leaks, performance degradation
    4. Log all errors and performance metrics
  • Expected Results:
    • No system crashes or unrecoverable errors
    • Memory usage remains stable (no leaks)
    • Performance maintains within acceptable bounds
  • Pass Criteria: System completes 24-hour test without failure
  • Requirements Verified: REQ-NF-R-001

3.3 Integration Requirements Testing

3.3.1 REQ-I-002: ROS2 Message Interface

Test Case TC-I-002-01: ROS2 Message Publishing

  • Objective: Verify correct ROS2 message publishing for poses and maps
  • Prerequisites: ROS2 environment, test subscribers
  • Test Steps:
    1. Initialize ROS2 integration
    2. Start SLAM processing
    3. Monitor published nav_msgs/Odometry messages
    4. Monitor published sensor_msgs/PointCloud2 messages
    5. Verify message format and frequency
  • Expected Results:
    • Odometry messages published at processing frame rate
    • Point cloud messages published at configured rate
    • All message fields correctly populated
    • Valid timestamps and coordinate frames
  • Pass Criteria: All message types published correctly with valid data
  • Requirements Verified: REQ-I-002, REQ-F-008

4. Performance Testing

4.1 Performance Test Scenarios

4.1.1 Baseline Performance Testing

Scenario: Standard indoor office environment with good lighting

Metric Target Measurement Method
Processing FPS ≥30 FPS Frame timing analysis
End-to-end Latency <100ms Timestamp comparison
Memory Usage <512MB Process monitoring
CPU Usage <80% average System resource monitoring
GPU Utilization >80% when available GPU monitoring tools

4.1.2 Stress Testing Scenarios

High Feature Density:

  • Test environment: Highly textured scene with 2000+ detectable features
  • Expected behavior: System maintains frame rate through feature filtering

Low Light Conditions:

  • Test environment: Dimly lit scene with minimal features
  • Expected behavior: Graceful degradation with increased tracking robustness

Rapid Motion:

  • Test environment: Fast camera motion with motion blur
  • Expected behavior: Robust tracking with predictive pose estimation

Memory Pressure:

  • Test scenario: Limited memory environment (2GB total)
  • Expected behavior: Adaptive quality reduction and memory cleanup

4.2 Embedded System Performance

4.2.1 Raspberry Pi 4 Testing

Hardware Configuration:

  • Raspberry Pi 4 Model B (8GB RAM)
  • ARM Cortex-A72 quad-core 1.5GHz
  • microSD card storage
  • USB 3.0 stereo camera

Performance Targets:

Metric Target Acceptable
Processing FPS ≥15 FPS ≥10 FPS
Memory Usage <1GB <1.5GB
CPU Usage <80% <90%
Power Consumption <5W <7W

4.2.2 NVIDIA Jetson Testing

Hardware Configuration:

  • NVIDIA Jetson Nano/Xavier
  • ARM Cortex-A57/A78AE
  • Integrated GPU
  • CSI camera interface

Performance Targets:

Metric Target Acceptable
Processing FPS ≥30 FPS ≥20 FPS
GPU Utilization >70% >50%
Memory Usage <2GB <3GB
Power Efficiency >10 FPS/W >5 FPS/W

5. Test Environment Requirements

5.1 Hardware Requirements

5.1.1 Development Testing Environment

Minimum Configuration:

  • CPU: Intel Core i5-8th gen or AMD Ryzen 5 3600
  • RAM: 16GB DDR4
  • Storage: 500GB SSD
  • GPU: GTX 1660 or equivalent (optional)
  • Cameras: USB 3.0 stereo camera or individual calibrated cameras

Recommended Configuration:

  • CPU: Intel Core i7-10th gen or AMD Ryzen 7 5700X
  • RAM: 32GB DDR4
  • Storage: 1TB NVMe SSD
  • GPU: RTX 4070 or equivalent
  • Cameras: High-quality stereo camera with hardware synchronization

5.1.2 Performance Testing Environment

High-End Configuration:

  • CPU: Intel Core i9-12900K or AMD Ryzen 9 5900X
  • RAM: 64GB DDR4-3200
  • Storage: 2TB NVMe SSD
  • GPU: RTX 4080 or RX 7800 XT
  • Cameras: Industrial stereo camera system

5.2 Software Requirements

5.2.1 Operating System Support

Platform Versions Test Priority
Ubuntu Linux 20.04 LTS, 22.04 LTS Primary
Windows 10, 11 Secondary
macOS 12+, Apple Silicon support Tertiary
Raspberry Pi OS 64-bit, latest Embedded

5.2.2 Development Tools

Required Tools:

  • Python 3.9+ with pip and virtual environment support
  • Git version control
  • CMake 3.16+ for native extensions
  • OpenCV 4.5+ with Python bindings
  • PyQt6/PySide6 for GUI testing

Testing Tools:

  • pytest for unit testing
  • pytest-benchmark for performance testing
  • pytest-cov for coverage analysis
  • memory_profiler for memory analysis
  • ROS2 tools for integration testing

6. Test Execution

6.1 Test Execution Schedule

6.1.1 Development Testing (Continuous)

Daily Execution:

  • Unit tests (all modules)
  • Basic integration tests
  • Performance regression tests
  • Code coverage analysis

Weekly Execution:

  • Complete integration test suite
  • GUI functional tests
  • Embedded platform tests
  • Memory and resource leak tests

6.1.2 Release Testing (Per Release Cycle)

Pre-Release Testing (2 weeks before release):

  • Complete functional test suite
  • Performance benchmarking
  • Multi-platform compatibility testing
  • Stress testing and reliability validation

Release Candidate Testing (1 week before release):

  • User acceptance testing
  • Final performance validation
  • Documentation and example verification
  • Production environment testing

6.2 Test Automation

6.2.1 Continuous Integration Pipeline

# GitHub Actions CI/CD Pipeline
name: Python-SLAM Test Suite

on: [push, pull_request]

jobs:
  unit-tests:
    runs-on: [ubuntu-latest, windows-latest, macos-latest]
    steps:
      - uses: actions/checkout@v3
      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.9'
      - name: Install dependencies
        run: pip install -r requirements-test.txt
      - name: Run unit tests
        run: python -m pytest tests/unit/ -v --cov

  integration-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Setup test environment
        run: ./scripts/setup-test-env.sh
      - name: Run integration tests
        run: python -m pytest tests/integration/ -v

  performance-tests:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Setup performance testing
        run: ./scripts/setup-perf-test.sh
      - name: Run performance benchmarks
        run: python -m pytest tests/performance/ --benchmark-only

6.2.2 Automated Test Reporting

Test Metrics Collection:

  • Test execution time and results
  • Code coverage percentage
  • Performance benchmark results
  • Memory usage and leak detection
  • Platform-specific test results

Reporting Dashboard:

  • Real-time test status display
  • Historical trend analysis
  • Performance regression alerts
  • Coverage trend monitoring
  • Failure rate tracking

7. Acceptance Criteria

7.1 Functional Acceptance

Requirement Category Acceptance Threshold Measurement
Core SLAM Functions 100% requirements verified All functional tests pass
GUI Components 100% core features working All GUI tests pass
Integration Points All interfaces validated Integration tests pass
Configuration All options functional Configuration tests pass

7.2 Performance Acceptance

Performance Metric Target Minimum Acceptable
Real-time Processing 30 FPS 25 FPS
Processing Latency <100ms <150ms
Memory Usage (Desktop) <512MB <768MB
Memory Usage (Embedded) <256MB <384MB
GPU Acceleration Speedup >3x >2x
Startup Time <5 seconds <10 seconds

7.3 Reliability Acceptance

Reliability Metric Target Minimum Acceptable
Continuous Operation 24+ hours 12+ hours
Mean Time Between Failures >24 hours >12 hours
Recovery Time from Failure <30 seconds <60 seconds
Memory Leak Rate 0 MB/hour <1 MB/hour

8. Risk Assessment and Mitigation

8.1 Test Risks

Risk Probability Impact Mitigation Strategy
Hardware Availability Medium High Multiple test environments, cloud testing
Performance Variability High Medium Statistical analysis, multiple test runs
Platform Differences Medium Medium Automated multi-platform testing
External Dependencies Low High Mocking and simulation frameworks
Test Data Quality Low Medium Synthetic data generation, validation

8.2 Quality Gates

Pre-Commit Quality Gate:

  • All unit tests pass
  • Code coverage >80%
  • No critical static analysis issues
  • Performance regression tests pass

Pre-Release Quality Gate:

  • All functional tests pass
  • Performance targets met
  • No open high-priority bugs
  • Documentation updated and reviewed

9. Test Deliverables

9.1 Test Documentation

Document Purpose Delivery Schedule
Test Plan (this document) Testing strategy and approach Development start
Test Procedures Detailed test execution steps Before testing start
Test Report Results and analysis After each test cycle
Coverage Report Code coverage analysis Weekly
Performance Report Benchmark results and trends Monthly

9.2 Test Artifacts

Test Code:

  • Unit test suites for all modules
  • Integration test scenarios
  • Performance benchmark scripts
  • Test data generation tools
  • Test environment setup scripts

Test Results:

  • Test execution logs and results
  • Performance benchmark data
  • Coverage analysis reports
  • Bug reports and tracking
  • Test metrics and trends

Document End

This Software Test Plan is maintained as part of the Python-SLAM configuration management system. All test procedures and results must follow the established quality assurance processes.