Probabilistic Failure Analysis in Model Validation & Verification

TL;DR

This paper explores probabilistic methods for fault localization in model validation, introducing a suspiciousness factor based on Kullback-Leibler Divergence and employing Hidden Markov Models to identify and rank potential design faults.

Contribution

It proposes novel probabilistic approaches, including a suspiciousness measure and HMM-based fault localization, for improved automated fault detection in models.

Findings

Effective ranking of faulty transitions using the suspiciousness factor.

HMM-based method accurately locates design faults in simulation models.

Provides confidence evaluation for fault localization results.

Abstract

Automated fault localization is an important issue in model validation and verification. It helps the end users in analyzing the origin of failure. In this work, we show the early experiments with probabilistic analysis approaches in fault localization. Inspired by the Kullback-Leibler Divergence from Bayesian probabilistic theory, we propose a suspiciousness factor to compute the fault contribution for the transitions in the reachability graph of model checking, using which to rank the potential faulty transitions. To automatically locate design faults in the simulation model of detailed design, we propose to use the statistical model Hidden Markov Model (HMM), which provides statistically identical information to component's real behavior. The core of this method is a fault localization algorithm that gives out the set of suspicious ranked faulty components and a backward algorithm that computes the matching degree between the HMM and the simulation model to evaluate the confidence degree of the localization conclusion.