Hierarchical featured state machines

Fragal, Vanderson Hafemann; Simão, Adenilso da Silva; Mousavi, Mohammad Reza

doi:10.1016/j.scico.2018.10.001

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…The communication techniques and agents involved in these elements are shown in Table 2. The statechart of the unit task agent adopts the design of a hierarchical state machine [28]. This design allows the unit task agent to function in two ways: a) Horizontal simulation for the working state of each unit task is in the mission profile.…”

Section: Support Resourcementioning

confidence: 99%

Warship Mission Reliability Modeling and Simulation from the Perspective of Equipment Support Resource

Wen

Peng

Chen

2023

JMSE

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To address the complex role of equipment support resource allocation on mission reliability during warship missions, this study establishes a warship mission reliability simulation system by using agent-based modeling to simulate warship task flow, equipment reliability structures, fault rules, and equipment support resource allocation schemes. The simulation system has the characteristics of multielement, modular, flexible configuration and easy operation. The key metrics relevant to different task conditions, such as the mission reliability of multistage and multistate systems, and the number of recommended allocations of equipment support resources, can be calculated by the system. In addition, sensitivity analyses of task parameters, equipment reliability structure, and the number of spare parts is carried out, and the effects on task reliability are discussed. The influencing factors considered in the modeling and calculation of mission reliability are expanded in this study, which can provide technical support for the optimal allocation of shipboard equipment support resources during the use phase of a warship, and the supportability design of warship equipment during the development phase.

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Section: Support Resourcementioning

confidence: 99%

Warship Mission Reliability Modeling and Simulation from the Perspective of Equipment Support Resource

Wen

Peng

Chen

2023

JMSE

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“…Recently, FFSMs have been employed to generate configurable test suites that can be pruned using feature constraints and product configurations [56]. The readability of FFSMs also has been improved by grouping up conditional states and transitions into hierarchical entities [55]. Thus, FFSMs have the prospect of serving as a suitable models basis for family-based analysis.…”

Section: S a V E G A M E [ S ] P A U S E G A M E P O N G [ N ] B O W ...mentioning

confidence: 99%

Learning by sampling: learning behavioral family models from software product lines

2021

Self Cite

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Family-based behavioral analysis operates on a single specification artifact, referred to as family model, annotated with feature constraints to express behavioral variability in terms of conditional states and transitions. Family-based behavioral modeling paves the way for efficient model-based analysis of software product lines. Family-based behavioral model learning incorporates feature model analysis and model learning principles to efficiently unify product models into a family model and integrate the behavior of various products into a behavioral family model. Albeit reasonably effective, the exhaustive analysis of product lines is often infeasible due to the potentially exponential number of valid configurations. In this paper, we first present a family-based behavioral model learning techniques, called FFSM Diff . Subsequently, we report on our experience on learning family models by employing product sampling. Using 105 products of six product lines expressed in terms of Mealy machines, we evaluate the precision of family models learned from products selected from different settings of the T-wise product sampling criterion. We show that product sampling can lead to models as precise as those learned by exhaustive analysis and hence, reduce the costs for family model learning.

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“…The Hierarchical Finite State Machine (HFSM) [19] adds elements such as state variables and state transitions on the basis of FSM to further express the dynamic behavior of the system at a finer granularity. The HFSM uses state variables to hierarchically manage a limited number of states and uses a top-down approach to describe the system.…”

Section: Related Workmentioning

confidence: 99%

“…In order to simplify the design and implementation of complex control logic, some recent studies have proposed an implementation method based on a hierarchical state machine [19,20]. The hierarchical state machine provides a top-down description of complex control logic, which improves the readability of the model and simplifies the design of complex control logic.…”

Section: Introductionmentioning

confidence: 99%

DoubleDeck: Decoupling Complex Control Logic of Network Protocols to Facilitate Efficient Hardware Implementation

Yang

Yan

et al. 2020

Electronics

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A hierarchical method is often used to simplify the design of complex logic. However, when the hierarchical method is used to implement protocol control, there is no suitable decoupling method to divide the sophisticated protocol control into a series of small control logics. For this reason, this paper designs and implements the DoubleDeck model composed of a top state machine and a set of bottom state machines. The model divides the protocol control logic into protocol state conversion logic and processing logic based on hierarchical idea. Simultaneously, DoubleDeck also provides a bottom deck mapping mechanism (BDM) for all protocol control processing logic. Based on the BDM mechanism, developers can quickly implement the bottom state machine array. Next, we present the hardware design of DoubleDeck and prototype the time synchronization protocol on an FPGA array. The results show that DoubleDeck can be used as a design model to guide the implementation of complex protocol control logic. Compared with the finite state machine (FSM), DoubleDeck uses the BDM mechanism to implement three protocols can reduce the amount of code for developers by 30%.

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Hierarchical featured state machines

Cited by 6 publications

References 30 publications

Warship Mission Reliability Modeling and Simulation from the Perspective of Equipment Support Resource

Warship Mission Reliability Modeling and Simulation from the Perspective of Equipment Support Resource

Learning by sampling: learning behavioral family models from software product lines

DoubleDeck: Decoupling Complex Control Logic of Network Protocols to Facilitate Efficient Hardware Implementation

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