Combining Genetic Programming and Model Checking to Generate Environment Assumptions

TL;DR

This paper presents an automated method combining genetic programming and model checking to infer environment assumptions for complex cyber-physical systems, improving accuracy and flexibility over existing techniques.

Contribution

It introduces a novel approach that learns assumptions involving arithmetic expressions over multiple variables, balancing soundness and informativeness, for CPS models.

Findings

Outperforms state-of-the-art assumption learning techniques.

Successfully infers assumptions close to expert-developed ones in industrial CPS.

Demonstrates effectiveness on benchmark and real satellite system models.

Abstract

Software verification may yield spurious failures when environment assumptions are not accounted for. Environment assumptions are the expectations that a system or a component makes about its operational environment and are often specified in terms of conditions over the inputs of that system or component. In this article, we propose an approach to automatically infer environment assumptions for Cyber-Physical Systems (CPS). Our approach improves the state-of-the-art in three different ways: First, we learn assumptions for complex CPS models involving signal and numeric variables; second, the learned assumptions include arithmetic expressions defined over multiple variables; third, we identify the trade-off between soundness and informativeness of environment assumptions and demonstrate the flexibility of our approach in prioritizing either of these criteria. We evaluate our approach using a public domain benchmark of CPS models from Lockheed Martin and a component of a satellite control system from LuxSpace, a satellite system provider. The results show that our approach outperforms state-of-the-art techniques on learning assumptions for CPS models, and further, when applied to our industrial CPS model, our approach is able to learn assumptions that are sufficiently close to the assumptions manually developed by engineers to be of practical value.