Abstract

The conformance testing problem for dynamical systems asks, given two dynamical models (e.g., as Simulink diagrams), whether their behaviors are “close” to each other. In the semi-formal approach to conformance testing, the two systems are simulated on a large set of tests, and a metric, defined on pairs of real-valued, real-timed trajectories, is used to determine a lower bound on the distance. We show how the Skorokhod metric on continuous dynamical systems can be used as the foundation for conformance testing of complex dynamical models. The Skorokhod metric allows for both state value mismatches and timing distortions, and is thus well suited for checking conformance between idealized models of dynamical systems and their implementations. We demonstrate the robustness of the metric by proving a transference theorem: trajectories close under the Skorokhod metric satisfy “close” logical properties in the timed linear time logic FLTL (Freeze LTL) containing a rich class of temporal and spatial constraint predicates involving time and value freeze variables. We provide efficient window-based streaming algorithms to compute the Skorokhod metric for both piecewise affine and piecewise constant traces, and use these as a basis for a conformance testing tool for Simulink. We experimentally demonstrate the effectiveness of our tool in finding discrepant behaviors on a set of control system benchmarks, including an industrial challenge problem.

Highlights

  • A fundamental question in model-based design is conformance testing: whether two models of a system display similar behavior

  • In this work we present a methodology for quantifying conformance between real-valued dynamical systems based on the Skorokhod metric [14]

  • We prove a logic transference result with respect to this logic: flows which are close under the Skorokhod metric satisfy “close” FLTL formulae

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Summary

Introduction

A fundamental question in model-based design is conformance testing: whether two models of a system display similar behavior. In this work we present a methodology for quantifying conformance between real-valued dynamical systems based on the Skorokhod metric [14]. Tractability We improve on recent polynomial-time algorithms for the Skorokhod metric [27] between polygonal (piecewise affine and continuous) traces by taking advantage of the fact that, in practice, only retimings that map the times in one trace to “close” times in the other are of interest This enables us to obtain a streaming sliding-window based monitoring procedure which takes only O(W ) time per sample, where W is the window size (assuming the dimension n of the system to be a constant).

Systems and conformance testing
The Skorokhod metric
Skorokhod metric computation: piecewise constant traces
Skorokhod metric computation: polygonal traces
Transference of logical properties
The logic TLTL
Transference of TLTL properties for propositional traces
Transference of FLTL properties for Rn-valued traces
Quantifying timing distortion using the Skorokhod metric
Experimental evaluation
Skorokhod distance between systems: case studies
Findings
Conclusion
Full Text
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