Test Harness

The test runner is the central execution engine that loads test cases, sequences their execution, and manages their lifecycle. In robotics, this component must handle complex dependencies and stateful operations. The orchestrator extends this by managing distributed tests across multiple compute nodes or physical hardware, synchronizing the start of perception tests with motion commands, for example. It interfaces with the scheduler to queue jobs and with the result aggregator to compile outputs.

These are software substitutes for real system components that are unavailable, non-deterministic, or unsafe to test against directly. In a robotic test harness, mocks are critical for:

• Sensor Emulators: Generate synthetic LiDAR point clouds, camera images, or IMU data with injected faults.
• Actuator Stubs: Simulate motor controllers or grippers, returning idealized position feedback.
• Hardware Abstraction Layer (HAL) Mocks: Allow control algorithms to be tested without physical motors or drivers.
• External Service Fakes: Simulate network APIs for mapping services or fleet management servers. Their use enables Software-in-the-Loop (SIL) and early-stage integration testing.

This subsystem is responsible for capturing, timestamping, and persistently storing all test artifacts. It is the foundation for observability and post-test analysis. Key data types collected include:

• Structured Logs: Execution traces, debug messages, and system events.
• Metrics: Quantitative performance data like latency (e.g., perception-to-planning loop time), accuracy (pose estimation error), and resource usage (CPU, memory).
• Telemetry: High-frequency time-series data from the system under test, such as joint angles, velocities, and calculated forces.
• Multimedia Outputs: Recorded video streams from simulated or real cameras, and ROS bag files. Data is often stored in formats compatible with analysis tools (e.g., SQL databases, Prometheus for metrics).

This component transforms raw test results into human-interpretable formats to support decision-making. It automates the creation of:

• Summary Reports: Pass/fail status, overall code coverage, and trend analysis.
• Detailed Failure Analysis: Stack traces, log snippets, and visual diffs (e.g., expected vs. actual robot trajectory).
• Compliance Documentation: Evidence trails for functional safety (FuSa) standards like ISO 26262.
• Interactive Dashboards: Real-time visualizations of test execution across a Continuous Integration/Continuous Deployment (CI/CD) pipeline, showing health metrics for different robotic modules (perception, planning, control).

A test harness must be adaptable to different system configurations, test scenarios, and environments. The configuration manager handles this through:

• Parameter Files: YAML or JSON files defining test parameters (e.g., simulation world, initial robot pose, object spawn locations).
• Environment Variables: Setting paths to model files, network addresses for hardware, and logging levels.
• Test Suite Definitions: Specifying which subsets of tests to run (e.g., all navigation tests, only fault-injection tests).
• Seeding for Reproducibility: Configuring random number generators in simulations to ensure deterministic execution of stochastic tests, which is critical for debugging.

This is the programmatic interface that allows external systems to command the test harness. It enables automation and integration into larger engineering workflows. Key functions include:

• Remote Triggering: Starting test suites from a CI/CD server like Jenkins or GitLab CI.
• Runtime Intervention: Pausing tests, injecting faults (fault injection), or modifying simulation parameters on-the-fly.
• Status Polling: Allowing monitoring tools to query the progress of long-running Hardware-in-the-Loop (HIL) tests.
• Result Retrieval: Providing an API for downstream systems to fetch test reports and artifacts for archival or further analysis. This turns the harness from an isolated tool into a connected component of the robotic system integration pipeline.

A testing technique where faults (e.g., sensor dropout, network latency spikes, hardware exceptions) are deliberately introduced into a system via the test harness to evaluate its robustness, error-handling, and fault recovery capabilities. This is critical for validating safety-critical systems.

• Types of Faults: Value faults (corrupt data), timing faults (delays), omission faults (lost messages).
• Objective: To ensure the system degrades gracefully and maintains a safe state.
• Methodology: Can be performed in SIL, HIL, or even on the physical system.

A system's ability to produce the same output, within a bounded and predictable time frame, for a given set of inputs and initial conditions. This property is essential for creating a reliable test harness, as non-determinism makes test results irreproducible and failures difficult to debug.

• Challenge in Testing: Asynchronous callbacks, thread scheduling, and network jitter can introduce non-determinism.
• Test Harness Design: Must control or record sources of randomness (e.g., seeding random number generators, using deterministic simulators).
• Critical For: Regression testing and comparing results across multiple test runs.