%0 Conference Proceedings
%T From Passive to Active FSM Inference via Checking Sequence Construction
%+ Centre de Recherche Informatique de Montréal = Computer Research Institute of Montréal (CRIM)
%A Petrenko, Alexandre
%A Avellaneda, Florent
%A Groz, Roland
%A Oriat, Catherine
%Z Part 2: Test Derivation Methods
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 29th IFIP International Conference on Testing Software and Systems (ICTSS)
%C St. Petersburg, Russia
%Y Nina Yevtushenko
%Y Ana Rosa Cavalli
%Y Hüsnü Yenigün
%I Springer International Publishing
%3 Testing Software and Systems
%V LNCS-10533
%P 126-141
%8 2017-10-09
%D 2017
%R 10.1007/978-3-319-67549-7_8
%K FSM testing
%K Machine inference
%K Machine identification
%K Active learning
%K Checking experiments
%K Checking sequences
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X The paper focuses on the problems of passive and active FSM inference as well as checking sequence generation. We consider the setting where an FSM cannot be reset so that its inference is constrained to a single trace either given a priori in passive inference scenario or to be constructed in active inference scenario or aiming at obtaining checking sequence for a given FSM. In each of the last two cases, the expected result is a trace representing a checking sequence for an inferred machine, if it was not given. We demonstrate that this can be achieved by a repetitive use of a procedure that infers an FSM from a given trace (identifying a minimal machine consistent with a trace) avoiding equivalent conjectures. We thus show that FSM inference and checking sequence construction can be seen as two sides of the same coin. Following an existing approach of constructing conjectures by SAT solving, we elaborate first such a procedure and then based on it the methods for obtaining checking sequence for a given FSM and inferring a machine from a black box. The novelty of our approach is that it does not use any state identification facilities. We only assume that we know initially the input set and a bound on the number of states of the machine. Experiments with a prototype implementation of the developed approach using as a backend an existing SAT solver indicate that it scales for FSMs with up to a dozen of states and requires relatively short sequences to identify the machine.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-01678991/document
%2 https://inria.hal.science/hal-01678991/file/449632_1_En_8_Chapter.pdf
%L hal-01678991
%U https://inria.hal.science/hal-01678991
%~ LIG_GLSI_VASCO
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-ICTSS
%~ IFIP-LNCS-10533