Replicable Simulation-Based Robot Validation through Provenance

Robot behavior is often validated through simulation-based testing, yet the replicability of such campaigns depends critically on transparent documentation of how tests are configured, executed, and post-processed. We argue that data provenance, coupled with the FAIR principles (findability, accessibility, interoperability, and reusability), addresses this gap by explicitly tracking links between artifacts and by attaching machine-readable metadata about file origins and key design decisions. Moreover, provenance and metadata cannot be treated as an afterthought confined to final datasets; they must be integrated into the testing processes that generate those datasets so that evidence can be reconstructed end-to-end. We demonstrate this by augmenting an existing simulation-based testing framework with provenance tracking and metadata collection mechanisms, and by using these extensions to enrich a mobile robot navigation dataset with structured provenance and FAIR-aligned metadata. Finally, we discuss obstacles encountered in this integration -- such as vocabulary alignment, attribute selection, and adoption of domain standards -- and provide actionable recommendations for implementing provenance-centric, FAIR metadata in robotics validation workflows.

Before Autonomy Takes Control: Software Testing in Robotics

Robotic systems are complex and safety-critical software systems. As such, they need to be tested thoroughly. Unfortunately, robot software is intrinsically hard to test compared to traditional software, mainly since the software needs to closely interact with hardware, account for uncertainty in its operational environment, handle disturbances, and act highly autonomously. However, given the large space in which robots operate, anticipating possible failures when designing tests is challenging. This paper presents a mapping study by considering robotics testing papers and relating them to the software testing theory. We consider 247 robotics testing papers and map them to software testing, discussing the state-of-the-art software testing in robotics with an illustrated example, and discuss current challenges. Forming the basis to introduce both the robotics and software engineering communities to software testing challenges. Finally, we identify open questions and lessons learned.