Theorem-proving distributed algorithms with dynamic analysis
Author(s)Ne Win, Toh, 1979-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Michael D. Ernst and Stephen J. Garland.
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Theorem provers are notoriously hard to use because of the amount of human interaction they require, but they are important tools that can verify infinite state distributed systems. We present a method to make theorem-proving safety properties of distributed algorithms more productive by reducing human intervention. We model the algorithms as I/O automata, render the automata executable, and analyze the test executions with dynamic invariant detection. The human work in using a theorem prover is reduced because our technique provides two forms of assistance: lemmas generated by the dynamic invariant detection for use in the prover; and prover scripts, or tactics, generated from our experience with the I/O automaton model and the knowledge embedded in the test suite used for execution. We test our technique on three case studies: the Peterson 2-process mutual exclusion algorithm, a strong caching implementation of shared memory, and Lamport's Paxos algorithm for distributed consensus. In the development and implementation of our method, we also improved the tools for formal verification of 1/0 automata and for dynamic invariant detection. We describe a new model for specifying I/O automata in the Isabelle theorem prover's logic, and prove the soundness of a technique for verifying invariants in this model in the Isabelle prover. We develop methods for generating proofs of I/0 automata for two theorem provers, the Larch Prover and Isabelle/HOL. We show methods for executing I/O automata for testing, by allowing the execution of some automata defined with universal and existential quantifiers that were previously non-executable. Lastly, we present improvements to dynamic invariant detection in order to make it more scalable - in particular, we show how to achieve efficient incremental dynamic invariant detection, where the detection tool is only allowed to make one pass over its input executions.
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 185-194).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.