Automated Repair of Cyber-Physical Systems

TL;DR

This research develops scalable automated program repair techniques tailored for cyber-physical systems, aiming to improve fault localization and repair efficiency using AI and spectrum-based methods.

Contribution

It introduces a novel combination of spectrum-based fault localization with AI-driven patch generation specifically for CPSs, addressing existing limitations.

Findings

Enhanced fault localization accuracy in CPSs

Reduced test execution times for repairs

Effective repair patches generated for industrial CPS code

Abstract

Cyber-Physical Systems (CPS) integrate digital technologies with physical processes and are common in different domains and industries, such as robotic systems, autonomous vehicles or satellites. Debugging and verification of CPS software consumes much of the development budget as it is often purely manual. To speed up this process, Automated Program Repair (APR) has been targeted for a long time. Although there have been advances in software APR and CPS verification techniques, research specifically on APR for CPSs is limited. This Ph.D. research project aims to develop scalable APR techniques for CPSs, addressing problems of fault localization, long test execution times, and fitness function limitations. A new method combining spectrum-based fault localization (SBFL) with patch generation and advanced artificial intelligence techniques will be investigated. The approach will be validated by empirical studies on open and industrial code bases of CPSs.