Modeling and Analyzing Adaptive User-Centric Systems in Real-Time Maude

TL;DR

This paper presents a formal modeling and analysis framework for adaptive user-centric systems using Real-Time Maude, enabling verification of time-dependent properties through model checking and simulation.

Contribution

It introduces a generic component-based modeling approach and analysis algorithms for real-time, adaptive systems using Metric Temporal Logic in Real-Time Maude.

Findings

Model checking of time-bounded response properties is feasible.

The approach is sound and complete for maximal time sampling.

Application demonstrated with an adaptive advertising scenario.

Abstract

Pervasive user-centric applications are systems which are meant to sense the presence, mood, and intentions of users in order to optimize user comfort and performance. Building such applications requires not only state-of-the art techniques from artificial intelligence but also sound software engineering methods for facilitating modular design, runtime adaptation and verification of critical system requirements. In this paper we focus on high-level design and analysis, and use the algebraic rewriting language Real-Time Maude for specifying applications in a real-time setting. We propose a generic component-based approach for modeling pervasive user-centric systems and we show how to analyze and prove crucial properties of the system architecture through model checking and simulation. For proving time-dependent properties we use Metric Temporal Logic (MTL) and present analysis algorithms for model checking two subclasses of MTL formulas: time-bounded response and time-bounded safety MTL formulas. The underlying idea is to extend the Real-Time Maude model with suitable clocks, to transform the MTL formulas into LTL formulas over the extended specification, and then to use the LTL model checker of Maude. It is shown that these analyses are sound and complete for maximal time sampling. The approach is illustrated by a simple adaptive advertising scenario in which an adaptive advertisement display can react to actions of the users in front of the display.