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Testing noninterference, quickly

Part of: JFP Research Articles

Published online by Cambridge University Press:  05 April 2016

CĂTĂLIN HRIŢCU ,
LEONIDAS LAMPROPOULOS ,
ANTAL SPECTOR-ZABUSKY ,
ARTHUR AZEVEDO DE AMORIM ,
MAXIME DÉNÈS ,
JOHN HUGHES ,
BENJAMIN C. PIERCE  and
DIMITRIOS VYTINIOTIS
CĂTĂLIN HRIŢCU
Affiliation:
Inria Paris, Prosecco team, Paris, France (e-mail: [email protected])
LEONIDAS LAMPROPOULOS
Affiliation:
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA
ANTAL SPECTOR-ZABUSKY
Affiliation:
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA
ARTHUR AZEVEDO DE AMORIM
Affiliation:
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA
MAXIME DÉNÈS
Affiliation:
Inria Paris, Gallium team, Paris, France
JOHN HUGHES
Affiliation:
Computer Science and Engineering, Chalmers University, Gothenburg, Sweden
BENJAMIN C. PIERCE
Affiliation:
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA
DIMITRIOS VYTINIOTIS
Affiliation:
Programming Principles and Tools group, Microsoft Research, Cambridge, UK
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Abstract

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Information-flow control mechanisms are difficult both to design and to prove correct. To reduce the time wasted on doomed proof attempts due to broken definitions, we advocate modern random-testing techniques for finding counterexamples during the design process. We show how to use QuickCheck, a property-based random-testing tool, to guide the design of increasingly complex information-flow abstract machines, leading up to a sophisticated register machine with a novel and highly permissive flow-sensitive dynamic enforcement mechanism that is sound in the presence of first-class public labels. We find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important for efficient testing. We propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. We also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. Taken together, our best methods enable us to quickly and automatically generate simple counterexamples for more than 45 bugs. Moreover, we show how testing guides the discovery of the sophisticated invariants needed for the noninterference proof of our most complex machine.

Type
Articles
Information
Journal of Functional Programming , Volume 26 , 2016 , e4
Copyright
Copyright © Cambridge University Press 2016 

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