Quantitative Verification with Adaptive Uncertainty Reduction

TL;DR

This paper introduces VERACITY, an iterative tool-supported approach that reduces uncertainty in stochastic model parameters through adaptive data collection, improving the accuracy of system verification under limited data conditions.

Contribution

The paper presents VERACITY, a novel adaptive uncertainty reduction heuristic integrated with confidence-interval verification for more accurate nonfunctional system verification.

Findings

VERACITY effectively reduces epistemic uncertainty in model parameters.

The approach improves verification accuracy with fewer data collection iterations.

Application to real systems demonstrates efficiency and effectiveness.

Abstract

Stochastic models are widely used to verify whether systems satisfy their reliability, performance and other nonfunctional requirements. However, the validity of the verification depends on how accurately the parameters of these models can be estimated using data from component unit testing, monitoring, system logs, etc. When insufficient data are available, the models are affected by epistemic parametric uncertainty, the verification results are inaccurate, and any engineering decisions based on them may be invalid. To address these problems, we introduce VERACITY, a tool-supported iterative approach for the efficient and accurate verification of nonfunctional requirements under epistemic parameter uncertainty. VERACITY integrates confidence-interval quantitative verification with a new adaptive uncertainty reduction heuristic that collects additional data about the parameters of the verified model by unit-testing specific system components over a series of verification iterations. VERACITY supports the quantitative verification of discrete-time Markov chains, deciding which components are to be tested in each iteration based on factors that include the sensitivity of the model to variations in the parameters of different components, and the overheads (e.g., time or cost) of unit-testing each of these components. We show the effectiveness and efficiency of VERACITY by using it for the verification of the nonfunctional requirements of a tele-assistance service-based system and an online shopping web application.