# Automated Verification and Synthesis of Embedded Systems using Machine Learning

## TL;DR

This paper explores how machine learning can be used to verify and synthesize reliable embedded systems, addressing the increasing complexity and correctness challenges in safety-critical applications.

## Contribution

It discusses recent advances in applying machine learning techniques for the verification and synthesis of embedded systems, emphasizing correct-by-construction design methods.

## Key findings

- Machine learning improves reliability in embedded system development.
- ML-based approaches facilitate correct-by-construction synthesis.
- Reliability issues in micro-grids and cyber-physical systems are effectively addressed.

## Abstract

The dependency on the correct functioning of embedded systems is rapidly growing, mainly due to their wide range of applications, such as micro-grids, automotive device control, health care, surveillance, mobile devices, and consumer electronics. Their structures are becoming more and more complex and now require multi-core processors with scalable shared memory, in order to meet increasing computational power demands. As a consequence, reliability of embedded (distributed) software becomes a key issue during system development, which must be carefully addressed and assured. The present research discusses challenges, problems, and recent advances to ensure correctness and timeliness regarding embedded systems. Reliability issues, in the development of micro-grids and cyber-physical systems, are then considered, as a prominent verification and synthesis application. In particular, machine learning techniques emerge as one of the main approaches to learn reliable implementations of embedded software for achieving a correct-by-construction design.