🤖 AI Summary
Quantifying the frequency with which safety-critical properties hold across multiple simulation variants of hybrid programs—such as those in medical devices and autonomous vehicles—remains challenging. Method: This paper extends the Lince tool by introducing, for the first time, a parallel execution mechanism for multiple simulation variants and an attribute frequency statistical analysis module. Leveraging a C-style modeling language with native support for differential equations, the approach enables hybrid system modeling, integrates scheduling optimization, and automatically generates histograms representing the distribution of property satisfaction frequencies over large sets of simulation trajectories. Contributions/Results: (1) It proposes the first statistical verification framework tailored to hybrid programs with multiple variants; (2) it enables frequency-based verification of safety properties and identification of behavioral patterns; and (3) in an empirical evaluation on an adaptive cruise control system, it significantly enhances statistical interpretability under uncertainty and design variability.
📝 Abstract
Hybrid systems are increasingly used in critical applications such as medical devices, infrastructure systems, and autonomous vehicles. Lince is an academic tool for specifying and simulating such systems using a C-like language with differential equations. This paper presents recent experiments that enhance Lince with mechanisms for executing multiple simulation variants and generating histograms that quantify the frequency with which a given property holds. We illustrate our extended Lince using variations of an adaptive cruise control system.