[Paper] Scenario-based System Testing for Distributed Robotics Applications

Source: arXiv - 2604.25772v1

Overview

The paper introduces SCenario Specification Language (SCSL), a new DSL and toolchain for automatically generating and running system‑level tests on highly distributed, collaborative robotics applications. By letting engineers describe scenarios instead of a monolithic model, SCSL makes testing feasible for systems that are too complex, too nondeterministic, or dynamically reconfigurable for traditional model‑based approaches.

Key Contributions

• SCSL DSL – a concise, scenario‑oriented language that captures expected robot behaviours, stimuli, and dynamic reconfiguration actions.
• Composable Scenarios – support for sequential and parallel composition, enabling complex end‑to‑end tests to be built from reusable building blocks.
• Online (on‑the‑fly) Test Execution – the runtime engine decides the next test step during execution, handling nondeterminism without exhaustive pre‑computation.
• Collaboration Construct – a first‑class primitive for adding, removing, or rewiring robot components while a test is in progress, reflecting real‑world fleet dynamics.
• Prototype Tool Platform – an implementation that parses SCSL, generates executable test harnesses, and demonstrates the approach on a multi‑robot salvage mission scenario.

Methodology

1. Scenario Modeling – Engineers write SCSL scripts that describe what should happen (e.g., “robot A must locate the wreck”) and how to interact with the system (e.g., “inject a sensor fault”).
2. Composition – Simple scenarios are combined using ; (sequential) or || (parallel) operators, allowing test designers to assemble sophisticated mission‑level tests without redefining low‑level behaviours.
3. Runtime Engine – The SCSL interpreter monitors the distributed system, matches observed events against the scenario specification, and decides the next action dynamically. This avoids the need for a complete state‑space model.
4. Dynamic Reconfiguration – The collaborate construct lets the engine react to components joining or leaving (e.g., a robot battery dies) and automatically rewires the test flow, keeping the test valid despite topology changes.
5. Evaluation – The authors apply the language to a simulated salvage mission involving several cooperating robots, automatically generating a test run that exercises fault injection, coordination, and reconfiguration.

Results & Findings

• Successful End‑to‑End Test Generation – The prototype generated a full test execution for the salvage mission without any manual orchestration.
• Scalability – By splitting the overall test into independent scenarios, the approach sidestepped the combinatorial explosion that plagues monolithic model‑based testing.
• Robustness to Nondeterminism – Online decision making allowed the test to adapt to unpredictable robot actions (e.g., varying path planning outcomes) while still verifying high‑level mission goals.
• Dynamic Adaptation – When a robot was programmatically removed from the fleet during the test, the collaboration construct automatically rewired the remaining robots, and the test continued without failure.

Practical Implications

• Faster CI/CD for Robot Fleets – Teams can embed SCSL‑based tests into continuous integration pipelines, automatically validating coordination logic each time code is pushed.
• Reduced Test Maintenance – Reusable scenario fragments mean that adding a new robot type or sensor only requires updating the relevant scenario, not rewriting whole system models.
• Live Fault Injection – Developers can simulate sensor failures, communication drops, or component loss during a running mission, helping to harden fault‑tolerant algorithms.
• Dynamic Deployment Support – Cloud‑orchestrated robot swarms (e.g., warehouse fulfillment or disaster response) can be tested while nodes join/leave, mirroring production conditions.
• Cross‑Domain Applicability – Although demonstrated on robots, the same concepts apply to any distributed cyber‑physical system—autonomous drones, edge‑computing clusters, or IoT sensor networks.

Limitations & Future Work

• Prototype Scope – The current toolchain is a proof‑of‑concept; it lacks integration with popular robotics middleware (e.g., ROS 2) and large‑scale simulation environments.
• Performance Overhead – Online monitoring and dynamic rewiring introduce runtime overhead that may affect time‑critical missions; profiling and optimization are needed.
• Formal Guarantees – While the language semantics are defined, the paper does not provide automated verification that a given SCSL script fully covers all intended behaviours.
• Future Directions – The authors plan to (1) extend SCSL with richer data‑flow constructs, (2) build adapters for ROS 2 and DDS, and (3) explore automated test‑case minimization to reduce execution time for large fleets.

Authors

• Jan Peleska
• Felix Brüning
• Wen-Ling Huang
• Anne E. Haxthausen

Paper Information

• arXiv ID: 2604.25772v1
• Categories: cs.SE
• Published: April 28, 2026
• PDF: Download PDF