51,640 research outputs found
Abstraction and Learning for Infinite-State Compositional Verification
Despite many advances that enable the application of model checking
techniques to the verification of large systems, the state-explosion problem
remains the main challenge for scalability. Compositional verification
addresses this challenge by decomposing the verification of a large system into
the verification of its components. Recent techniques use learning-based
approaches to automate compositional verification based on the assume-guarantee
style reasoning. However, these techniques are only applicable to finite-state
systems. In this work, we propose a new framework that interleaves abstraction
and learning to perform automated compositional verification of infinite-state
systems. We also discuss the role of learning and abstraction in the related
context of interface generation for infinite-state components.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455
Compositional Verification for Timed Systems Based on Automatic Invariant Generation
We propose a method for compositional verification to address the state space
explosion problem inherent to model-checking timed systems with a large number
of components. The main challenge is to obtain pertinent global timing
constraints from the timings in the components alone. To this end, we make use
of auxiliary clocks to automatically generate new invariants which capture the
constraints induced by the synchronisations between components. The method has
been implemented in the RTD-Finder tool and successfully experimented on
several benchmarks
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