Parallel Algorithms for Testing Finite State Machines:Generating UIO Sequences

Hierons, Robert M.; Türker, Uraz Cengiz

doi:10.1109/tse.2016.2539964

Cited by 19 publications

(20 citation statements)

References 38 publications

(43 reference statements)

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“…Moreover, recently researchers proposed a parallel algorithm to accelerate the execution of a FA [35,36]. Recently massively parallel algorithms have also been presented for deriving state identification sequences from finite state machines [37,38]. Karahoda et al recently represented a fast version of an existing greedy RS generation algorithm ( [4]) for deterministic complete automata [39].…”

Section: Problem Statementmentioning

confidence: 99%

Parallel brute-force algorithm for deriving reset sequences from deterministic incomplete finite automata

Türker¹

2019

Turk J Elec Eng & Comp Sci

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A reset sequence (RS) for a deterministic finite automaton A is an input sequence that brings A to a particular state regardless of the initial state of A . Incomplete finite automata (FA) are strong in modeling reactive systems, but despite their importance, there are no works published for deriving RSs from FA. This paper proposes a massively parallel algorithm to derive short RSs from FA. Experimental results reveal that the proposed parallel algorithm can construct RSs from FA with 16,000,000 states. When multiple GPUs are added to the system the approach can handle larger FA.

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Section: Problem Statementmentioning

confidence: 99%

Parallel brute-force algorithm for deriving reset sequences from deterministic incomplete finite automata

Türker¹

2019

Turk J Elec Eng & Comp Sci

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“…In recent years, Hierons and Trker have been using GPUs to accelerate the generation of testing sequences for FSMs based on unique I/O sequences [14], state harmonised state identifiers and characterising sets [13] and distinguishing sequences [15]. As far as we are aware, there is no existing work in using GPUs to accelerate the execution of FSM tests.…”

Section: Related Workmentioning

confidence: 99%

Accelerated Finite State Machine Test Execution Using GPUs

Yaneva

Kapoor

Rajan

et al. 2018

2018 25th Asia-Pacific Software Engineering Conference (APSEC)

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Model-based development has emerged as a popular approach aiding automation of the software development process, where software is implemented and tested based on a model of the required system. Finite State Machines (FSMs) are a widely used model representation for a variety of systems, including control systems, signal processing and communications protocols. Ensuring that the model accurately represents the required behaviour involves the generation and execution of a large number of tests that is time consuming and expensive. In this paper, we focus on test execution and propose exploiting Graphics Processing Units (GPUs) for accelerating FSM testing by executing the tests in parallel on GPU threads. Our approach includes methods to encode the FSM efficiently and optimise the layout of tests in GPU memory for fast execution. We compare speedup achieved by our approach against parallel test execution on a multi-core CPU with 16 cores. We also assess the improvement in speedup using the proposed FSM encoding and test layouts. We use large FSMs from the networking domain and a large industry FSM from Keysight, who provide electronic measurement solutions, in our evaluation. We accelerate the execution of test suites providing all-transition pair coverage for each of the FSMs. Speedup achieved is subject to characteristics of the FSM and associated tests, and is greatly improved with efficient FSM encoding and test layout in memory. We find our approach on the GPU achieves a maximum test execution speedup of 12× over a 16-core CPU.

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“…Unfortunately, UIOS can be exponential long [29] and so there has been interest in methods that relatively efficiently generate UIOS [30], [31], [32], [33]. In order to increase scalability and speed up UIO derivation, Hierons and T ürker introduced a massively parallel algorithm to construct UIOS from DFSMs [34]. The P-UIO algorithm scaled well; it required only a few seconds to construct UIOS from FSMs with a million states.…”

Section: Takedownmentioning

confidence: 99%