5 research outputs found
Synthesizing Finite-state Protocols from Scenarios and Requirements
Scenarios, or Message Sequence Charts, offer an intuitive way of describing
the desired behaviors of a distributed protocol. In this paper we propose a new
way of specifying finite-state protocols using scenarios: we show that it is
possible to automatically derive a distributed implementation from a set of
scenarios augmented with a set of safety and liveness requirements, provided
the given scenarios adequately \emph{cover} all the states of the desired
implementation. We first derive incomplete state machines from the given
scenarios, and then synthesis corresponds to completing the transition relation
of individual processes so that the global product meets the specified
requirements. This completion problem, in general, has the same complexity,
PSPACE, as the verification problem, but unlike the verification problem, is
NP-complete for a constant number of processes. We present two algorithms for
solving the completion problem, one based on a heuristic search in the space of
possible completions and one based on OBDD-based symbolic fixpoint computation.
We evaluate the proposed methodology for protocol specification and the
effectiveness of the synthesis algorithms using the classical alternating-bit
protocol.Comment: This is the working draft of a paper currently in submission.
(February 10, 2014
Learning Moore Machines from Input-Output Traces
The problem of learning automata from example traces (but no equivalence or
membership queries) is fundamental in automata learning theory and practice. In
this paper we study this problem for finite state machines with inputs and
outputs, and in particular for Moore machines. We develop three algorithms for
solving this problem: (1) the PTAP algorithm, which transforms a set of
input-output traces into an incomplete Moore machine and then completes the
machine with self-loops; (2) the PRPNI algorithm, which uses the well-known
RPNI algorithm for automata learning to learn a product of automata encoding a
Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore
machine using PTAP extended with state merging. We prove that MooreMI has the
fundamental identification in the limit property. We also compare the
algorithms experimentally in terms of the size of the learned machine and
several notions of accuracy, introduced in this paper. Finally, we compare with
OSTIA, an algorithm that learns a more general class of transducers, and find
that OSTIA generally does not learn a Moore machine, even when fed with a
characteristic sample
Decoupled Fitness Criteria for Reactive Systems
The correctness problem for reactive systems has been thoroughly explored and
is well understood. Meanwhile, the efficiency problem for reactive systems has
not received the same attention. Indeed, one correct system may be less fit
than another correct system and determining this manually is challenging and
often done ad hoc. We (1) propose a novel and general framework which
automatically assigns comparable fitness scores to reactive systems using
interpretable parameters that are decoupled from the system being evaluated,
(2) state the computational problem of evaluating this fitness score and reduce
this problem to a matrix analysis problem, (3) discuss symbolic and numerical
methods for solving this matrix analysis problem, and (4) illustrate our
approach by evaluating the fitness of nine systems across three case studies,
including the Alternating Bit Protocol and Two Phase Commit.Comment: v2 notable changes: - updated discussion of "component separable" -
updated presentation/organization of section 4 - updated
presentation/organization of section 5 - added new case stud