Testing deterministic implementations from nondeterministic FSM specifications

Petrenko, Alexandre; Yevtushenko, Nina; Bochmann, G.V.

doi:10.1007/978-0-387-35062-2_10

Cited by 91 publications

(105 citation statements)

References 14 publications

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“…The second line is to study different methods for reducing the size of the generated test suite. We plan to consider the adaption of state counting [40,39], to deal with conformance, recently introduced in [23] and adapt it to the context of TSXMs, more specifically, to its stochastic extension.…”

Section: Discussionmentioning

confidence: 99%

Testing timed systems modeled by Stream X-machines

2009

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Stream X-machines have been used to specify real systems requiring to represent complex data structures. They are a kind of extended finite state machine using a shared memory to specify communications between the components of systems. In this paper we introduce an extension of the Stream X-machines formalism in order to specify systems that present temporal requirements. On the one hand, we consider that (output) actions take time to be performed. On the other hand, our formalism allows to specify timeouts. Timeouts represent the time a system can wait for the environment to react. Since timeous affect the set of available actions of the system, a relation focusing on the functional behavior of systems, that is, the actions that they can perform, must explicitly take into account the possible timeouts. We also propose a formal testing methodology allowing to systematically test a system with respect to a specification. Finally, we introduce a test derivation algorithm. Given a specification, the derived test suite is sound and complete, that is, a system under test successfully passes the test suite if and only if this system conforms to the specification.

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Section: Discussionmentioning

confidence: 99%

Testing timed systems modeled by Stream X-machines

2009

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“…A deterministic finite state machine (DFSM) is an NFSM in which for every state s and input x, |f (s, x)| ≤ 1. There has been much interest in testing from a DFSM (see, for example, [13,26,3]) or an NFSM (see, for example, [38,46,31,24]). See [35] for a survey.…”

Section: Probabilistic Finite State Machinesmentioning

confidence: 99%

Mutation testing from probabilistic and stochastic finite state machines

Hierons

Merayo

2009

Journal of Systems and Software

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Specification mutation involves mutating a specification, and for each mutation a test is derived that distinguishes the behaviours of the mutated and original specifications. This approach has been applied with finite state machines based models. This paper extends mutation testing to finite state machine models that contain non-functional properties. The paper describes several ways of mutating a finite state machine with probabilities (PFSM) or stochastic time (PSFSM) attached to their transitions and shows how test sequences that distinguish between them and their mutants can be generated. Testing then involves applying each test sequence multiple times, observing the resultant output sequences and using results from statistical sampling theory in order to compare the observed frequency of each output sequence with that expected.

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“…Most such techniques use state identification sequences that distinguish the states of M [8], [17], [18], [19], [20], [21], [22], [23]. For example, a test technique can check that the input of x in state s leads to output y and state s as follows: start with a preamble (input sequence)x that takes M to s, then apply x, check that the output produced is y, and finally use one or more input sequences to distinguish the expected state s from all other states of M .…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithms for Generating Harmonised State Identifiers and Characterising Sets

Hierons

Türker

2016

IEEE Trans. Comput.

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Abstract-Many automated finite state machine (FSM) based test generation algorithms require that a characterising set (CS) or a set of harmonised state identifiers (HSIs) is first produced. The only previously published algorithms for partial FSMs were brute-force algorithms with exponential worst case time complexity. This paper presents polynomial time algorithms and also massively parallel implementations of both the polynomial time algorithms and the brute-force algorithms. In the experiments the parallel algorithms scaled better than the sequential algorithms and took much less time. Interestingly, while the parallel version of the polynomial time algorithm was fastest for most sizes of FSMs, the parallel version of the brute-force algorithm scaled better due to lower memory requirements.

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Testing deterministic implementations from nondeterministic FSM specifications

Cited by 91 publications

References 14 publications

Testing timed systems modeled by Stream X-machines

Testing timed systems modeled by Stream X-machines

Mutation testing from probabilistic and stochastic finite state machines

Parallel Algorithms for Generating Harmonised State Identifiers and Characterising Sets

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