State Machine Model Research Articles

An EFSM-Based Test Data Generation Approach in Model-Based Testing

Testing is an integral part of software development. Current fast-paced system developments have rendered traditional testing techniques obsolete. Therefore, automated testing techniques are needed to adapt to such system developments speed. Model-based testing (MBT) is a technique that uses system models to generate and execute test cases automatically. It was identified that the test data generation (TDG) in many existing model-based test case generation (MB-TCG) approaches were still manual. An automatic and effective TDG can further reduce testing cost while detecting more faults. This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model (EFSM). The proposed approach integrates MBT with combinatorial testing. The information available in an EFSM model and the boundary value analysis strategy are used to automate the domain input classifications which were done manually by the existing approach. The results showed that the proposed approach was able to detect 6.62 percent more faults than the conventional MB-TCG but at the same time generated 43 more tests. The proposed approach effectively detects faults, but a further treatment to the generated tests such as test case prioritization should be done to increase the effectiveness and efficiency of testing.

A contextual sensor system for non-intrusive machine status and energy monitoring

Event-driven contexts in manufacturing occur pervasively as a result of interactions among involved entities such as machines, workers, materials, and environment. One of the primary tasks in smart manufacturing is to derive a context-aware system conveniently incorporating worker knowledge for generating timely actionable intelligence for workers on factory floor and supervisors to respond. In this paper, we propose to design a human-and-machine interaction recognition framework by using a causality concept to collect contextual data for classifications of normal and abnormal machine operations. The causes and effects are between workers and machines for this initial research. To apply the causality to recognize worker interactions, initially a reliable way to identify the states of machines is necessary. The proposed contextual sensor system, consisting of a power meter for measuring machine operation conditions, a visual camera for capturing worker and machine interactions via a finite state machine model, and an algorithm for determining power signatures of individual components via energy disaggregation is implemented on semiconductor fabrication machines (manual or PLC controlled) each with multiple components. The experiment results demonstrate its context extraction capability such as components states and their corresponding energy usage in real time as well as its ability to identify anomalous operation conditions.

A progressive learning method on unknown protocol behaviors

Reverse analyzing of unknown protocol behaviors keeps being a tough nut in Protocol Reverse Engineering (PRE), which infers specifications of unknown protocols by observable information, especially when only transmitted messages are available. This paper proposes a novel protocol state machine model Stochastic Protocol finite-state Transducer (SPT) to describe the message interaction rules between communicating terminals in a probabilistic way attempting to simulate behavior rules of unknown protocols in certain implementation. Together with a state related field recognition and compensation method, a progressive SPT learning algorithm of unknown protocols named Sptia-PL, is designed and implemented to reconstruct the SPT of target protocol with the ability to predict succeeding behaviors. By updating the SPT progressively, the proposed method is able to learn continuously in linear time and remain the established model in optimal condition during the whole learning process. This strategy thoroughly avoids the state explosion problem existing in most state machine learning methods of PRE. Experiments on two open and three local collected datasets of FTP, SMTP and POP3 prove the rationality of SPT model and effectiveness of Sptia-PL algorithm by an average Accuracy over 0.94 and a Coverage close to 0.99. The small computing cost O(N) of this method and high confidence of results outperforms all the known state-of-the-art methods significantly.

VECTOR-DRIVEN LOGIC AND STRUCTURE FOR TESTING AND DEDUCTIVE FAULT SIMULATION

Context. It is known that data structures are decisive for the creation of efficient parallel algorithms and high-performance computing devices. Therefore, the development of mathematically perfect and technologically simple data structures takes about 80 percent of the design time, when about 20 percent of time and material resources are spent on algorithms and their hardware-software coding. This lead to search for such primitives of data structures that will significantly simplify the parallel high-performance algorithms which are working on them. Models and methods for testing and simulation of digital systems are proposed, which containing certain advantages of quantum computing in terms of implementation of vector qubit data structures in technology of classical computational processes.
 Objective. The goal of the work is development of an innovative technology for qubit-vector synthesis and deductive analysis of tests for their verification based on vector data structures that greatly simplify algorithms that can be embedded as BIST components in digital systems on chips.
 Method. The deductive faults simulation is used to obtain analytical expressions focused on transporting fault lists through a functional or logical element based on the xor-operation, which serves as a measure of similarity-difference between a test, a function and faults which is specified in the same way in one of the formats − a table, graph, equation. A binary vector is proposed as the most technologically advanced primitive of data structures for setting logical functionality for the purpose of parallel synthesis and analysis of digital systems. The parallelism of solving combinatorial problems is a physical property of quantum computing, which in classical computing, for parallel simulation and faults diagnostics, is provided by unitary-coded data structures due to excess memory.
 Results. 1) A method of analytical synthesis of deductive logic for functional elements on the gate level and register transfer level has been developed. 2) A deductive processor for faults simulation based on transporting input lists or faults vectors to external outputs of digital circuits was proposed. 3) The qubit-vector form of logic setting and methods of qubit synthesis of deductive equations for faults simulation were described. 4) A qubit-vector method for the tests’ synthesis which is using derivatives calculated by vector coverage of logic has been developed. 5) Models and methods verification is performed on test examples in the software implementation of structures and algorithms.
 Conclusions. The scientific novelty lies in the new paradigm of the technology for the synthesis of deductive RTL logic based on metric test equation, which forms the. A vector form for structures description is introduced, which makes it possible to apply wellknown technologies for the synthesis and analysis of logical circuits tests to effectively solve the problems of graph structures testing and state machine models of digital devices. The practical significance is reflected in the examples of analytical synthesis of deductive logic for functional elements on gate level and register transfer level. A deductive processor for faults simulation which is focused on implementation as a BIST tool, which is used in online testing, simulation and fault diagnosis for digital systems on chips is proposed. A qubit-vector form of the digital systems description is proposed, which surpasses the existing methods of computing devices development in terms of the metric: manufacturability, compactness, speed and quality. A software application has been developed that implements the main testing, simulation and diagnostics services which are used in the educational process to study the advantages of qubit-vector data structures and algorithms. The computational complexity of synthesis processes and deductive formulas for logic and their usage in fault simulation are given.

Parameter tampering cyberattack and event-trigger detection in game-based interactive demand response

The cybersecurity defense strength is weaker on the demand side (DS) than that in the power grid side, resulting in potential security threats on the power system, and the related security research is scared with DS. The vulnerability to the false data injection (FDI) attack in the DS under the framework of interactive demand response (IDR) is analyzed, and the process of the FDI attack to the user energy management system (UEMS) is introduced. Then a leader–follower game framework is proposed to model the FDI attacking process in UEMS, where the attacker is the leader while the prosumers under the IDR framework formulated as a non-cooperative game are followers. The parameters in the dynamic electricity cost functions are chosen to tamper in the FDI attacks for their good concealment and vital roles in IDR. The existence and uniqueness of the Stackelberg equilibrium are also proved. A distributed event trigger mechanism combined with a machine learning-based detecting method is proposed to detect the malicious cyberattack. The event trigger mechanism is implemented with a state machine model, and the attack detection is conducted in the energy retailer using the long short-term memory model. Finally, the attacking results and sensitivity of the parameters determined by the attacker on the attacking effects are analyzed in the case study, and the distributed event trigger detection mechanism is compared with the time trigger mechanism in terms of accuracy and detection time under different events trigger thresholds and attack rates.

Single-state state machines in model-driven software engineering: an exploratory study

ContextModels, as the main artifact in model-driven engineering, have been extensively used in the area of embedded systems for code generation and verification. One of the most popular behavioral modeling techniques is the state machine. Many state machine modeling guidelines recommend that a state machine should have more than one state in order to be meaningful. However, single-state state machines (SSSMs) violating this recommendation have been used in modeling cases reported in the literature.ObjectiveWe aim for understanding the phenomenon of using SSSMs in practice as understanding why developers violate the modeling guidelines is the first step towards improvement of modeling tools and practice.MethodTo study the phenomenon, we conducted an exploratory study which consists of two complementary studies. The first study investigated the prevalence and role of SSSMs in the domain of embedded systems, as well as the reasons why developers use them and their perceived advantages and disadvantages. We employed the sequential explanatory strategy, including repository mining and interview, to study 1500 state machines from 26 components at ASML, a leading company in manufacturing lithography machines from the semiconductor industry. In the second study, we investigated the evolutionary aspects of SSSMs, exploring when SSSMs are introduced to the systems and how developers modify them by mining the largest state-machine-based component from the company.ResultsWe observe that 25 out of 26 components contain SSSMs. Our interviews suggest that SSSMs are used to interface with the existing code, to deal with tool limitations, to facilitate maintenance and to ease verification. Our study on the evolutionary aspects of SSSMs reveals that the need for SSSMs to deal with tool limitations grew continuously over the years. Moreover, only a minority of SSSMs have been changed between SSSM and multiple-state state machine (MSSM) during their evolution. The most frequent modifications developers made to SSSMs is inserting events with constraints on the execution of the events.ConclusionsBased on our results, we provide implications for developers and tool builders. Furthermore, we formulate hypotheses about the effectiveness of SSSMs, the impacts of SSSMs on development, maintenance and verification as well as the evolution of SSSMs.

Efficient reconfigurable architecture for moving object detection with motion compensation

The detection and tracking of object in large data surveillance requires a proper motion estimation and compensation techniques which are generally used to detect accurate movement from video stream. In this paper, a novel hardware level architecture involving motion detection, estimation, and compensation is proposed for real-time implementation. The motion vectors are obtained using 16×16 sub-blocks with a novel parallel D flip flop architecture in this work to arrive at an optimised architecture. The sum of absolute difference (SAD) is then calculated by optimized absolute difference and adder blocks designed using kogge-stone adder which helps in improving the speed of the architecture. The controller block is designed by finite state machine model used for synchronization of all the operations. Further, the comparator and compensation blocks are optimized by using basic logical elements and the Kogge-stone adder. Finally, the proposed architecture is implemented on Zynq Z7-10 field-programmable gate array (FPGA) and simulated using System Generator tool for real time traffic signal. The hardware and software parameters are compared with the existing techniques which shows that the proposed architecture is efficient than existing methods of design.

유한상태기계를 이용한 소프트스위칭 계통연계형 인버터 제어 알고리즘에 관한 연구

This paper present a finite state machine (FSM) model of a trajectory control algorithm for single-stage soft-switching grid-tied inverters with a fast dynamic response. A Grid-tied inverter for single photovoltaic (PV) panel that interfaces distributed energy sources and grid is targeted as an application in this paper. The single-stage soft-switching operation is implemented to reduce switching losses using a resonance filter between two switching bridges. The proposed FSM model effectively selects the switching state of a converter in complex DC / AC bidirectional operations with active/reactive power control. The control scheme directly limits the amount of energy in the resonant circuit and provides a fast transient response with minimal switching actions in all quadrants on v<SUB>ac</SUB> - i<SUB>ac</SUB> plane. The proposed model is digitally implemented on a TI digital signal processor and verified by Hardware-In-the-Loop (HIL) tests.

A privacy‐preserving mobile location‐based advertising system for small businesses

AbstractSmall‐to‐medium businesses are always seeking affordable ways to advertise their products and services securely. With the emergence of mobile technology, it is possible than ever to implement innovative Location‐Based Advertising (LBS) systems using smartphones that preserve the privacy of mobile users. In this paper, we present a prototype implementation of such systems by developing a distributed privacy‐preserving system, which has parts executing on smartphones as a mobile app, as well as a web‐based application hosted on the cloud. The mobile app leverages Google Maps libraries to enhance the user experience in using the app. Mobile users can use the app to commute to their daily destinations while viewing relevant ads such as job openings in their neighborhood, discounts on favorite meals, etc. We developed a client‐server privacy architecture that anonymizes the mobile user trajectories using a bounded perturbation strategy. A multi‐modal sensing approach is proposed for modeling the context switching of the developed LBS system, which we represent as a Finite State Machine model. The multi‐modal sensing approach can reduce the power consumed by mobile devices by automatically detecting sensing mode changes to avoid unnecessary sensing. The developed LBS system is organized into two parts: the business side and the user side. First, the business side allows business owners to create new ads by providing the ad details, Geo‐location, photos, and any other instructions. Second, the user side allows mobile users to navigate through the map to see ads while walking, driving, bicycling, or quietly sitting in their offices. Experimental results are presented to demonstrate the scalability and performance of the mobile side. Our experimental evaluation demonstrates that the mobile app incurs low processing overhead and consequently has a small energy footprint.

ПАТЕРН «СТАН» ДЛЯ ВБУДОВАНИХ СИСТЕМ З МОЖЛИВІСТЮ ДИНАМІЧНОГО СТВОРЕННЯ СТАНІВ

Nowadays, embedded systems are technical systems that combine several programming technologies in the software part. A complex technical system of this type can be divided into two basic components, first – the main controller (MC). The second part is designed to control the mechanisms and is an auxiliary real-time controller (ARTC). The urgent task is to provide software connection of the MC software and the software part of the ARTC. This is especially true when the MC plays the role of artificial intelligence and when it may be necessary to add new states during program execution without restarting the ARTC software. The software part of the ARTC can be implemented in the form of a State pattern, which implements a mathematical model of a finite state machine. To connect the software part of the MC and ARTC and dynamically add new states to the ARTC program, the following tasks must be solved: - to develop a software solution of the pattern State for ARTC, which will allow to add states dynamically while working without stopping and rebooting on command from the MC; - test this solution on a practical microcontroller. According to the results of recent researches and publications, we can draw a general conclusion about the lack of a ready-made solution to the problem of adding states to the finite state machine without rebooting. This article is devoted to solving a new promising problem of connecting the software part of the MC with elements of artificial intelligence and ARTC for actuators. There is an unexplored part of the overall problem that needs to be solved in the context of the dynamic addition of new states to the finite state machine of the ARTC program. To solve the tasks, it is necessary to develop and test a prototype of the ARTC program in the form of a State pattern for the microcontroller architecture Cortex-M at the level of system programming, which allows you to add states during program execution dynamically. A prototype of the ARTC program was developed in the form of a finite state machine for the Cortex-M architecture microcontroller at the level of system programming in the form of a software solution of the State pattern, which allows to add states during program execution. The source code was developed and the formal model of the software decision in the form of the graph was constructed. The research was carried out and the work of the program was demonstrated. The proposed software solution allows software connection of the MC software and the software part of the ARTC in complex embedded systems. This is necessary when there may be a need to add new states dynamically during execution of software program without restarting the ARTC. Such a need may arise, for example, when the MC plays the role of artificial intelligence and there is a need to add new states during execution dynamically.

Generating feasible protocol test sequences from EFSM models using Monte Carlo tree search

Feasible test sequences generation is a key step in protocol conformance testing based on the Extended Finite State Machine (EFSM) model. To guarantee the feasibility of generated test sequences, transition executability analysis (TEA) technique is widely applied in automatic test derivation. However, the TEA method often suffers from the famous state explosion problem, which has become a major obstacle to its efficient application.

GAS LEAK SOURCE DETECTION ROBOT USING FINITE STATE MACHINE MODEL (FSM)

At this time, many gases are at risk in the surrounding area, especially in industrial areas, such as radiological materials and toxic gases that can contaminate the surrounding area as well as the occurrence of gas leaks. From the problem-created gas detection simulation robot using Finite State Machine (FSM) model, the robot can help human work in this case the robot helps detect gas leaks that if faced by humans will be dangerous. The robot was created to detect nearby gases and provide notifications in the event of a gas leak. The robot was tested in several areas to see if the robot could perform the task according to the input given, the trial was conducted 40 times with 4 different arenas, from 40 tests the robot can perform the correct task 37 times and error 3 times so that accuracy results obtained by 92.5%. The creation of this gas detection simulation robot is expected to be developed again as a gas leak detection inspection robot as an early warning system.

A Hierarchical State Machine Model for Hazard Analysis of Real-time Safety Critical Systems

Real-time systems must avoid hazardous situations. To achieve this, their functionality should be investigated under time constraints. In this paper, a modeling based on Hierarchical Communicating Real-time State Machine (H- CRSM) and analysis methodology is proposed to examine statically ANSI-C code to obtain the hazardous events in the input system. A hazardous event equation is taken as input to the proposed system. The output is a list of hazardous scenarios. A path in the code showing the cause of the undesirable event is associated with each hazardous scenario. The strength of the proposed methodology is that the process of hazardous situations detection does not require running the ANSI-C program multiple times with different input values. It also focuses on analyzing the software level of the life cycle. Most of the verification tools check the system level. The system level may be bug-free but the software level may not.

Effective Energy Management in Renewable Resources Integrated Grid Using Finite State Machine Model

Effective Energy Management in Renewable Resources Integrated Grid Using Finite State Machine Model

Developing Edu-Game “Ulun Smart-Kid” Learning Media of Banjar Language and Game Agent with Finite State Machine Model

Developing Edu-Game “Ulun Smart-Kid” Learning Media of Banjar Language and Game Agent with Finite State Machine Model

Autonomous Behavior Intelligence Control of Self-Evolution Mobile Robot for High-Voltage Transmission Line in Complex Smart Grid

In complex smart grid, the power maintenance robot is important equipment to ensure the reliable operation of high-voltage lines and it is a useful exploration to realize high-quality power transmission. In view of the increasingly prominent contradiction between the robot single operation function and the diversification of power grid maintenance operations, additional with the robot weak autonomous operation and intelligent behavior ability, this paper proposes a new configuration of a reconfigurable power robot with terminal functions and its autonomous operation behavior control method for the three typical tasks which are the high-voltage transmission line insulators, drainage plates, and dampers maintenance. Through the analysis and planning of the robot operation behavior, the robot finite state machine (FSM) model in the three operation states has been established. Through the introduction of the state transfer function in the FSM, the automatic switching control between the robot key operation states can be realized, and the robot motion planning can be optimized. The movement and working flow of the robot improve the robot operation intelligence and operation efficiency. Based on this, the robot autonomous operation control system has been designed and the robot physical prototype has been developed for three maintenance tasks of insulators, drainage plates, and dampers. Finally, simulation experiments and field operation tests verify the effectiveness and engineering practicability of the proposed method. Compared with traditional manual control, the autonomous behavior control method can significantly improve the robot operational efficiency and operational intelligence. At the same time, the robot multitask function and autonomous behavior control under different tasks can be realized and the method has strong versatility for different task objects and different line environments. The research and its promotion have important theoretical significance and practical application value for the power system operation and maintenance integration management.

Live modeling in the context of state machine models and code generation

Live modeling has been recognized as an important technique to edit behavioral models while being executed and helps in better understanding the impact of a design choice. In the context of Model-driven Development (MDD) models can be executed by interpretation or by the translation of models into existing programming languages, often by code generation. This work is concerned with the support of live modeling in the context of state machine models when they are executed by code generation. To this end, we propose an approach that is completely independent of any live programming support offered by the target language. This independence is achieved with the help of a model transformation which equips the model with support for features which are required for live modeling. A subsequent code generation then produces a self-reflective program that allows changes to the model elements at runtime (through synchronization of design and runtime models). We have applied the approach in the context of UML-RT and created a prototype (Live-UMLRT) that provides a full set of services for live modeling of UML-RT state machines such as re-execution, adding/removing states and transitions, and adding/removing action code. We have evaluated the prototype on several use-cases. The evaluation shows that (1) generation of a self-reflective and model instrumentation can be carried out with reasonable performance, and (2) our approach can apply model changes to the running execution faster than the standard approach that depends on the live programming support of the target language.

Exploiting big.LITTLE Batteries for Software Defined Management on Mobile Devices

Battery service time is a critical constraint on the availability and functionality of mobile devices. To address the challenge, we propose CAPMAN, a management framework that jointly optimizes the cooling and active power management in a smartphone, a typical mobile device, equipped with a hybrid battery pack. We establish the framework with three major designs. First, we abstract the correlation among the batteries, devices and software into a finite state machine model, whose state transitions can be triggered by actions like system calls and user activities. Second, we propose a battery scheduling algorithm that determines the more suitable battery for cooling/active power use, with respect to the dynamic software behaviors and their impact on the hardware states, based on Markov decision process (MDP). Third, we design a facility for joint cooling and active power management by coordinating TECs and batteries. With the three major designs, CAPMAN realizes software defined management that schedules heterogeneous batteries and TEC cooling in a timely manner. We prototype CAPMAN on multiple popular smartphones and a PYNQ development board. CAPMAN can achieve 114% longer battery service time under skewed loads; compared to baselines, CAPMAN shows 55% performance gain and 53% less energy use on average

Mining understandable state machine models from embedded code

Program understanding is a time-consuming and tedious activity for software developers. Manually building abstractions from source code requires in-depth analysis of the code. Automatic extraction of such models is possible, but cannot derive meaningful abstractions that are not already contained in the code. The automated extraction even has problems to decide which aspects of the code are important and which are not. Therefore, interactive semi-automatic approaches are the compromise of choice. In this article, we describe how state machines that describe the behaviour of a function can be extracted from code. The approach includes interaction – the user decides which aspects of the identified potentially relevant information is really relevant and which is not. This helps to reduce the resulting state machines to an understandable degree. However, these state machines in their raw form have transition conditions that are very complex and thus not understandable for humans. Therefore, we also introduce a technique to reduce these guards to an understandable form. The technique is a combination of heuristic logic minimization, exploitation of infeasible paths, and using transition priorities. We evaluate the approach on industrial embedded C code, first in a case study with hundreds of extracted state machines, and then in two experiments with professional developers. The results show that the approach is highly effective in making the guards understandable, and that guards reduced by our approach and presented with priorities are easier to understand than guards without priorities. We also show that the overall approach is beneficial for program comprehension. The guard reduction approach itself is quite generic and can also be applied to other problems. We demonstrate that for the simplification of mode switch logic.

NB-IoT optimization for smart meters networks of smart cities: Case study

The future demands for Smart Meters (SM) applications impose maximizing resources utilization. In this paper, we achieve two main objectives to comply with fifth-generation networks goals and serving the SM applications. First one is to enhance the Narrowband-Internet-of-Things spectral efficiency. The second is to alleviate the signalling burden during each transmission request for SMs. First, we model the uplink scheduler for the NB-IoT access network using the state-machine modelling methodology on the Simulink environment. The simulation results are verified and validated with trusted modelling technique. Second, we optimize the NB-IoT uplink scheduler, which exploits the periodicity nature of SMs applications. We achieve that by proposing an integrated scheduling protocol which rearranges the transmission times of different smart meters utilities, and it draws a map for the transmission schedule of them to better utilize the sparse time resources. The proposed approach comprises architecture, signalling, and algorithms. We evaluate the optimization in term of numbers of SMs versus four performance indicators; resources utilization, spectral efficiency, setup success percentage, and session drop rate according to a case study. The simulation results prove the ability of our proposal to increase the spectrum utilization to 17.47%. This enhancement reflects on spectral efficiency that improved doubled.