A Unified Approach for Static and Runtime Verification: Framework and Applications
Paper i proceeding, 2012

Static verification of software is becoming ever more effective and efficient. Still, static techniques either have high precision, in which case powerful judgements are hard to achieve automatically, or they use abstractions supporting increased automation, but possibly losing important aspects of the concrete system in the process. Runtime verification has complementary strengths and weaknesses. It combines full precision of the model (including the real deployment environment) with full automation, but cannot judge future and alternative runs. Another drawback of runtime verification can be the computational overhead of monitoring the running system which, although typically not very high, can still be prohibitive in certain settings. In this paper, we propose a framework to combine static analysis techniques and runtime verification with the aim of getting the best of both techniques. In particular, we discuss an instantiation of our framework for the deductive theorem prover KeY, and the runtime verification tool LARVA. Apart from combining static and dynamic verification, this approach also combines the data centric analysis of KeY with the control centric analysis of LARVA. An advantage of the approach is that, through the use of a single specification which can be used by both analysis techniques, expensive parts of the analysis could be moved to the static phase, allowing the runtime monitor to make significant assumptions, dropping parts of expensive checks at runtime. We also discuss specific applications of our approach.

Författare

Wolfgang Ahrendt

Chalmers, Data- och informationsteknik, Programvaruteknik

Gordon J. Pace

University of Malta

Gerardo Schneider

Göteborgs universitet

Lecture Notes in Computer Science

0302-9743 (ISSN)

312-326

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1007/978-3-642-34026-0_24

ISBN

978-3-642-34025-3