A Unified Framework for Runtime Verification and Model-Based Diagnosis in LOLA

2026-06-18

AI Summary

This work proposes the first unified formal framework that integrates runtime verification and model-based diagnosis, overcoming the limitations of traditional approaches that rely on disjoint toolchains and struggle to jointly support online fault detection and localization. Built upon the LOLA stream specification language, the method encodes system behavior, component health states, and observational data into a common stream-based representation. This formulation accommodates both time-invariant and transient faults and handles nondeterministic observations. By leveraging online stream processing and explicit fault semantics, the approach enables efficient, continuous end-to-end fault localization without requiring additional tools, thereby significantly enhancing the integration and practicality of runtime monitoring for complex systems.

Abstract

We present an integrated framework that unifies runtime verification and model-based diagnosis within the stream specification language LOLA. By encoding system descriptions, component health states, and observations into a single stream-based formalism, the approach enables continuous, online fault localization directly alongside fault detection, without requiring separate toolchains. The framework supports both time-invariant and transient faults, and naturally accommodates nondeterministic observations.

Problem

• runtime verification
• model-based diagnosis
• fault localization
• LOLA
• stream specification

Innovation

• runtime verification
• model-based diagnosis
• LOLA
• fault localization
• stream specification