We present Provengo, a comprehensive suite of tools designed to facilitate the implementation of Scenario-Driven Model-Based Testing (SDMBT), an innovative approach that utilizes scenarios to construct a model encompassing the user's perspective and the system's business value while also defining the desired outcomes. With the assistance of Provengo, testers gain the ability to effortlessly create natural user stories and seamlessly integrate them into a model capable of generating effective tests. The demonstration illustrates how SDMBT effectively addresses the bootstrapping challenge commonly encountered in model-based testing (MBT) by enabling incremental development, starting from simple models and gradually augmenting them with additional stories.

Despite the existence of powerful formal languages for writing robot controllers, most existing functional modules are written using standard programming languages. The existence of such a code base raises critical challenges: 1. How to enable automated analysis, monitoring, and reuse of existing code given that reasoning directly about code fragments is impractical. 2. How to convey to users the expected level of performance of an autonomous robot? 3. Perhaps most crucial: how to quickly identify abnormal behavior of autonomous robots? This is a key impediment to the deployment of such platforms in open environments. We address these issues through the use of performance-level profiles (PLPs), a formal, yet intuitive, language for specifying the expected properties of functional modules, designed with the above aims in mind. PLPs are motivated by action specification languages, such as PDDL 2.1, but add novel elements important for robotic applications, such as update frequency, run-time statistics, progress measures, and trigger conditions, and take into account the different roles modules can play. PLPs have been used to support monitoring in two projects: an autonomous compact track loader, and a service robot. Additionally, we developed a number of tools for automated monitoring-code generation from PLPs.