White-Box Validation of Collective Adaptive Systems by Statistical Model Checking and Process Mining

Modeling and analyzing collective adaptive systems with heterogeneous components poses challenges to language designers, software engineers, and computer scientists interested in the formal verification of the modeled systems. This requires the integration of computational, reasoning, and analysis tools, but also of techniques to validate and shed light on the actual behavior described by a model. We build on a methodology built in collaboration with Rocco De Nicola, based on his influential contributions in the modeling and programming of distributed and adaptive systems. Such methodology allows to model and analyze collective adaptive systems equipped with reasoning capabilities. It combines SCEL, a language for programming collective adaptive systems, with Pirlo, a reasoner that can enrich agents with reasoning capabilities, and MultiVeStA, a statistical model checker that can analyze the obtained models by simulating them. Here, we further enrich this methodology with techniques from process-oriented data science, and in particular process mining, to ease the validation and debugging of such models. This follows recent proposals from the literature that validate models by explaining graphically the behaviors observed by a statistical model checker. We demonstrate our approach by considering a simple collision-avoidance robotic scenario where a group of robots moves in an arena while aiming at minimizing the number of collisions.