Mining Environment Assumptions for Cyber-Physical System Models

TL;DR

This paper presents a method to automatically infer environment assumptions in cyber-physical systems by mining STL formulas from input-output data, aiding system verification.

Contribution

It introduces a novel algorithm that combines enumeration of PSTL formulas with decision-tree learning to extract environment assumptions from real-world data.

Findings

Successfully applied to transportation and healthcare data

Effectively identifies input conditions satisfying output requirements

Enhances formal verification of cyber-physical systems

Abstract

Many complex cyber-physical systems can be modeled as heterogeneous components interacting with each other in real-time. We assume that the correctness of each component can be specified as a requirement satisfied by the output signals produced by the component, and that such an output guarantee is expressed in a real-time temporal logic such as Signal Temporal Logic (STL). In this paper, we hypothesize that a large subset of input signals for which the corresponding output signals satisfy the output requirement can also be compactly described using an STL formula that we call the environment assumption. We propose an algorithm to mine such an environment assumption using a supervised learning technique. Essentially, our algorithm treats the environment assumption as a classifier that labels input signals as good if the corresponding output signal satisfies the output requirement, and as bad otherwise. Our learning method simultaneously learns the structure of the STL formula as well as the values of the numeric constants appearing in the formula. To achieve this, we combine a procedure to systematically enumerate candidate Parametric STL (PSTL) formulas, with a decision-tree based approach to learn parameter values. We demonstrate experimental results on real world data from several domains including transportation and health care.