Software Implementation Issues Research Articles

Machine Learning-Based Methods for Code Smell Detection: A Survey

Code smells are early warning signs of potential issues in software quality. Various techniques are used in code smell detection, including the Bayesian approach, rule-based automatic antipattern detection, antipattern identification utilizing B-splines, Support Vector Machine direct, SMURF (Support Vector Machines for design smell detection using relevant feedback), and immune-based detection strategy. Machine learning (ML) has taken a great stride in this area. This study includes relevant studies applying ML algorithms from 2005 to 2024 in a comprehensive manner for the survey to provide insight regarding code smell, ML algorithms frequently applied, and software metrics. Forty-two pertinent studies allow us to assess the efficacy of ML algorithms on selected datasets. After evaluating various studies based on open-source and project datasets, this study evaluated additional threats and obstacles to code smell detection, such as the lack of standardized code smell definitions, the difficulty of feature selection, and the challenges of handling large-scale datasets. The current studies only considered a few factors in identifying code smells, while in this study, several potential contributing factors to code smells are included. Several ML algorithms are examined, and various approaches, datasets, dataset languages, and software metrics are presented. This study provides the potential of ML algorithms to produce better results and fills a gap in the body of knowledge by providing class-wise distributions of the ML algorithms. Support Vector Machine, J48, Naive Bayes, and Random Forest models are the most common for detecting code smells. Researchers can find this study helpful in better anticipating and taking care of software development design and implementation issues. The findings from this study, which highlight the practical implications of ML algorithms in software quality improvement, will help software engineers fix problems during software design and development to ensure software quality.

Anomaly and Cyber Attack Detection Technique Based on the Integration of Fractal Analysis and Machine Learning Methods

In modern data transmission networks, in order to constantly monitor network traffic and detect abnormal activity in it, as well as identify and classify cyber attacks, it is necessary to take into account a large number of factors and parameters, including possible network routes, data delay times, packet losses and new traffic properties that differ from normal. All this is an incentive to search for new methods and techniques for detecting cyber attacks and protecting data networks from them. The article discusses a technique for detecting anomalies and cyberattacks, designed for use in modern data networks, which is based on the integration of fractal analysis and machine learning methods. The technique is focused on real-time or near-real-time execution and includes several steps: (1) detecting anomalies in network traffic, (2) identifying cyber attacks in anomalies, and (3) classifying cyber attacks. The first stage is implemented using fractal analysis methods (evaluating the self-similarity of network traffic), the second and third stages are implemented using machine learning methods that use cells of recurrent neural networks with a long short-term memory. The issues of software implementation of the proposed technique are considered, including the formation of a data set containing network packets circulating in the data transmission network. The results of an experimental evaluation of the proposed technique, obtained using the generated data set, are presented. The results of the experiments showed a rather high efficiency of the proposed technique and the solutions developed for it, which allow early detection of both known and unknown cyber attacks.

Temperature Distribution at the Border Astenosphere–Lithosphere (Mathematical Model)

The convection of matter in the Earth's upper mantle is considered, which in the Oberbeck–Boussinesq approximation is due to thermogravitational differentiation. Within the framework of this approximation, a 2-D numerical simulation of convective flows of the medium matter was performed. The equation for temperature follows from the entropy balance relation, where, due to taking into account the variable viscosity in the system, there is an effect of energy dissipation. The boundary conditions correspond to the assignment of the temperature generally accepted at the boundary of the upper and lower mantles, and for the lateral boundaries - their thermal insulation. At the asthenosphere–lithosphere boundary, assumptions were made that the heat dynamics is determined by its flow from the asthenosphere layer closest to the boundary, part of the heat dissipation along the boundary, and heat consumption for melting the lithosphere matter. Numerical solution of the constitutive equations is carried out in variables stream function - vorticity. An iterative scheme for their solution is given. The issues of software implementation of the numerical simulation apparatus are discussed. It is shown that under such boundary conditions, a quasi-periodic regime of heat oscillations is formed in the system under consideration.

A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational Intelligence Methods.

The article discusses an approach to the construction and operation of a proactive system for protecting smart power grids against cyberattacks on service data transfer protocols. It is based on a combination of computational intelligence methods: identifying anomalies in network traffic by evaluating its self-similarity, detecting and classifying cyberattacks in anomalies, and taking effective protection measures using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Fractal analysis, mathematical statistics, and neural networks with long short-term memory are used as tools in the development of this protection system. The issues of software implementation of the proposed system and the formation of a data set containing network packets of a smart grid system are considered. The experimental results obtained using the generated data set demonstrated and confirmed the high efficiency of the proposed proactive smart grid protection system in detecting cyberattacks in real or near real-time, as well as in predicting the impact of cyberattacks and developing efficient measures to counter them.

Цифровизация машиностроительного производства: технологическая подготовка, производство, прослеживание

The article deals with the aspects of digitalization at the stage of process-layout preparation (PLP). The necessity of partial carry of PLP tasks directly to the processing stage is proved. The issues of software and hardware implementation of intellectual technological systems focused on ensuring the required quality parameters, arrangement and software implementation of tracking, using "digital twins", mechanical production quality in the manufacture process and output monitoring within the use of finished products, are viewed. A schematic diagram of an intelligent manufacturing system based on NC control block Okuma OSP-P300L is presented.

Ensuring the survivability of embedded computer networks based on early detection of cyber attacks by integrating fractal analysis and statistical methods

The paper discusses a method for ensuring the survivability of embedded computer networks in conditions of cyber attacks, based on identifying anomalies in network traffic by assessing its self-similarity and determining the type of impact of cyber attacks using statistical methods. The proposed method includes three stages, at which the analysis of the self-similarity property for the reference traffic is performed (using the Dickey-Fuller test, R/S analysis and the DFA method), the analysis of the self-similarity property for real traffic (by the same methods) and additional processing of time series with statistical methods (moving average, Z-Score and CUSUM methods). The issues of software implementation of the proposed method and the formation of a data set containing network packets are considered. The experimental results demonstrated the presence of self-similarity in network traffic and confirmed the high efficiency of the proposed method. The method allows detecting cyber attacks in real or near real time and ensures high survivability of the embedded computer network.

Development of a Multi-sector Bounded-Neighbourhood Model with intersectoral commodity-money interactions

The article presents an approach to the development of an extended multi-sector bounded-neighbourhood with differentiated types of resources and intersectoral commodity-money interactions with implementation in the AnyLogic simulation tools. A feature of this approach is, firstly, a high degree of sectoral disaggregation (e.g., 42 types of economic activity are considered), and secondly, the three-level architecture of the proposed multi-agent system, which includes high-tech and low-tech resources as workplaces differentiated by types of economic activity (branches of the economy), and also multiple agents-individuals: natives and migrants with their own rules of behaviour and characteristics, and agents-sectors that provide the necessary structure of employment. We study the issues of software implementation of such a multi-level system in the AnyLogic, as well as the influence of the most important parameters of the model, in particular, such as the inflow rate of new migrants, dimensionality of forming industry clusters (i.e., a number of workplaces), level of wages by branches of the economy, etc. on the employment structure and the dynamics of gross domestic product, respectively.

КОМПЛЕКСНАЯ МЕТОДИКА ОБНАРУЖЕНИЯ КИБЕРАТАК НА ОСНОВЕ ИНТЕГРАЦИИ ФРАКТАЛЬНОГО АНАЛИЗА И СТАТИСТИЧЕСКИХ МЕТОДОВ

The article discusses a method for detecting cyber-attacks on computer networks based on detecting anomalies in network traffic by assessing its self-similarity and determining the impact of cyber-attacks using statistical methods. The proposed methodology provides for three stages, within which the analysis of the self-similarity property for reference traffic is performed (using the Dickey-Fuller test, R/S analysis and the DFA method), the analysis of the self-similarity property for real traffic (by the same methods) and additional processing of time series by statistical methods (moving average, Z-Score and CUSUM). The issues of software implementation of the proposed approach and the formation of a data set containing network packets are considered. The results of the experiments demonstrated the presence of self-similarity of network traffic and confirmed the high efficiency of the proposed method, which allows detecting cyber-attacks in real or near real time. Introduction: The use of information and communication technologies for information collection in modern computer networks makes it possible for an attacker to influence the network infrastructure by implementing cyber-attacks. Cyberattacks can achieve their goals due to the massive use of outdated operating systems, ineffective protection mechanisms and the presence of multiple vulnerabilities in unsecured network protocols. Such vulnerabilities give a potential attacker the ability to change the settings of network devices, listen and redirect traffic, block network interaction and gain unauthorized access to internal components of computer networks. The purpose of the work is to develop a methodology for detecting anomalies in network traffic by determining the degree of self-similarity of traffic using fractal analysis and statistical methods. Methods used: software implementation of the proposed methodology and the formation of a data set containing network packets. The results of the experiments demonstrated the presence of self-similarity of network traffic and confirmed the high efficiency of the proposed technique, which allows detecting cyber-attacks in real or near real time. The scientific novelty lies in the fact that the proposed methodology provides for three stages, within which the analysis of the self-similarity property for reference traffic is performed (using the Dickey-Fuller test, R/S analysis and the DFA method), the analysis of the self-similarity property for real traffic (by the same methods) and additional processing of time series by statistical methods (methods moving Average (MA), Z-Score and CUSUM). Result: the presented methodology allows detecting the impact of cyberattacks in real and close to real time, and the use of statistical methods increases the accuracy of determining cyberattacks. Practical significance: the presented methodology is universal and can be applied in the information exchange systems of public administration bodies performing the tasks of ensuring the security of the country.

Methodology for Management of the Protection System of Smart Power Supply Networks in the Context of Cyberattacks

This paper examines an approach that allows one to build an efficient system for protecting the information resources of smart power supply networks from cyberattacks based on the use of graph models and artificial neural networks. The possibility of a joint application of graphs, describing the features for the functioning of the protection system of smart power supply networks, and artificial neural in order to predict and detect cyberattacks is considered. The novelty of the obtained results lies in the fact that, on the basis of experimental studies, a methodology for managing the protection system of smart power supply networks in conditions of cyberattacks is substantiated. It is based on the specification of the protection system by using flat graphs and implementing a neural network with long short-term memory, which makes it possible to predict with a high degree of accuracy and fairly quickly the impact of cyberattacks. The issues of software implementation of the proposed approach are considered. The experimental results obtained using the generated dataset confirm the efficiency of the developed methodology. It is shown that the proposed methodology demonstrates up to a 30% gain in time for detecting cyberattacks in comparison with known solutions. As a result, the survivability of the Self-monitoring, Analysis and Reporting technology (SMART) grid (SG) fragment under consideration increased from 0.62 to 0.95.

Matters of Neural Network Repository Designing for Analyzing and Predicting of Spatial Processes

The article is devoted to solving the scientific problem of accumulating and systematizing models and machine learning algorithms by developing a repository of deep neural network models for analyzing and predicting of spatial processes in order to support the process of making managerial decisions in the field of ensuring conditions for sustainable development of regions. The issues of architecture development and software implementation of a repository of deep neural network models for spatial data analysis are considered, based on a new ontological model, which makes it possible to systematize models in terms of their application for solving design problems. An ontological model of a deep neural network repository for spatial data analysis is decomposed into the domain of deep machine learning models, problems being solved and data. Special attention is paid to the problems of storing data in the repository and the development of a subsystem for visualizing neural networks using a graph model. The authors have shown that for organizing a repository of deep neural network models, it is advisable to use a scientifically grounded set of database management systems integrated into a multi-model storage, characterizing the domains of using relational and NoSQL storages.

Research Methods in the Field of Computer Visualization Based on Virtual Reality

The paper examines research methods in the field of computer visualization based on virtual reality. Virtual reality environments were initially used for training simulation, but as far back as in the late 1980s scientific visualization systems based on virtual reality occurred. Those were the pilot versions of virtual test stands that were used in developing reusable space shuttles. Currently, virtual reality environments have been actively used in scientific visualization and software visualization systems. Developing such systems involves not only the issues of software implementation, modelling or adequate equipment choice, but also the tasks connected with cognitive processes occurring in both users and developers of visualization systems. Featured is a brief review of papers dealing with research in human interaction with virtual reality. The scope of tasks in developing visualization systems based on virtual reality is determined. The conclusion discusses several research issues, the solutions to which will increase the efficiency of interaction of users with virtual environments.

Hardware and Software Implementation of Constructive Geometric Models

The article is devoted to the consideration of a number of issues of hardware and software implementation of constructive geometric models. A rich arsenal of theoretical research in the field of constructive geometry has not been properly used for a long time due to the lack of tools for translating such models using computer technology. The development and improvement of the Simplex geometric modeling system, in which any geometric design is considered as a converter of information represented by signals of a geometric nature, has opened the possibility of applying the achievements of geometric science in computing applications, as well as the development of hardware that implements geometric calculation methods and provides a new graphical interface. The concept developed by the authors is aimed at creating specialized accelerators of geometric transformations.

Application of incremental satisfiability problem solvers for non-deterministic polynomial-time hard problems as illustrated by minimal Boolean formula synthesis problem

Subject of Research. The paper considers a method for solution of the nondeterministic polynomial hard problem (NP-hard problem) of a minimal Boolean formula synthesis from a given truth table. The solution of this problem is proposed based on its reduction to the Boolean satisfiability problem (SAT). The issues of efficient and convenient software implementation are discussed for reducing nondeterministic polynomial hard problems to the satisfiability problem. Method. For a minimal Boolean formula synthesis, the constraint programming approach was used: a SATformula was created for a given truth table, satisfiable if and only if, there exists a Boolean formula of a given size that satisfies the given truth table. The developed method accent is the application of incremental satisfiability problem solvers. Main Results. A method is proposed for synthesis of a Boolean formula, minimal with respect to the number of operators and terminals, for a given truth table. The method is based on reducing to satisfiability problem and provides the usage of incremental satisfiability problem solvers. The kotlin-satlib framework is developed with the possibility to use the Kotlin and Java languages effectively and conveniently for the software implementation of reducing various nondeterministic polynomial-time hard problems to satisfiability problem. Native interaction with satisfiability problem solvers by Java Native Interface (JNI) technology is used. The proposed method for the minimal Boolean formula synthesis is implemented in the Kotlin programming language using the developed kotlin-satlib framework. Practical Relevance. An experimental study on the example of minimal Boolean formula synthesis has shown that the usage of incremental satisfiability problem solvers for nondeterministic polynomial hard problems is reasonable, since it reduces the total solving time in comparison with the non-incremental approach application.

Compressed Principal Component Regression (C–PCR) Algorithm and FPGA Validation

To address the hardware and/or software implementation issues of principal component regression (PCR), we propose a novel algorithm called compressed PCR (C–PCR). C–PCR projects the input data to a lower dimensional space first, and then applies the compressed data to a significantly smaller PCR engine. We show that C–PCR can lower the computational complexity of PCR with a factor of compression ratio (CR) squared, i.e., CR2. Moreover, the output signal of C–PCR follows that of PCR with a small error, which increases with CR, when the projections are random. Using datasets of prerecorded brain neurochemicals, we experimentally show that C–PCR can achieve CRs as high as ~ 10. As far as hardware implementation is concerned, the experimental results show that reduction rates of 32% to 45% in different FPGA resources can be achieved using C–PCR.

Software and technical implementation of intelligent energy-saving control systems based on industrial controllers

Automation and control systems of technological objects are widely used in almost all industries currently. They allow continuous monitoring of the main operating parameters of the object, as well as provide a synthesis of control actions to achieve the best performance indicators of the technological installation, both in terms of performance and energy saving. Therefore, the problem of developing systems for optimal energy-saving control is very significant and relevant. The article deals with the issues of software and hardware implementation of energy-saving systems for managing complex technological objects. The generalized structure of the energy-saving control system software, obtained by decomposing the system into separate functional subsystems and program modules, is presented. The proposed software structure is largely universal and invariant to various control objects. The main components of the technical support of the energy saving control system are considered. A practical example of software and hardware implementation of the system of energy-saving control of dynamic modes of a group of heat engineering devices (multi-section roller-belt drying units) based on industrial controllers is shown.

Automated design, verification and testing of secure systems with embedded devices based on elicitation of expert knowledge

The rising significance and widening of embedded systems stipulate the importance of the security means against a great deal of computer security threats. Such systems involving a diversity of an-hoc embedded and mobile electronic devices functioning with the use of a broadband Internet access and even cloud technologies, are referenced conventionally as Internet of Things systems (IoT). Due to specificity of IoT systems the application of the combined security mechanisms requires their efficient energy and computing resource consumption, identification of potential conflicts and incompatibilities, control of information flows, monitoring anomalies of data in the system and other issues. At that an increased design complexity of IoT systems is determined by a low structuring and formalization of security knowledge in the field. We proposed an approach to identification of embedded security expert knowledge for its subsequent use in automated design, verification and testing tools for secure IoT systems. The paper encompasses the core elements of the proposed technique, namely security component configuring, revelation of implicit conflicts, verification of network information flows and abnormal data from sensors. The domain specific analysis of the field of embedded security is described. We also present the revealed expert knowledge used for configuration, verification and testing of embedded devices. Issues of software implementation and discussion are covered.

SMT: An interface for localized storm surge modeling

The devastation wrought by Hurricanes Katrina (2005), Ike (2008), and Sandy (2012) in recent years continues to underscore the need for better prediction and preparation in the face of storm surge and rising sea levels. Simulations of coastal flooding using physically based hydrodynamic codes like ADCIRC, while very accurate, are also computationally expensive, making them impractical for iterative design scenarios that seek to evaluate a range of countermeasures and possible failure points. We present a graphical user interface that supports local analysis of engineering design alternatives based on an exact reanalysis technique called subdomain modeling, an approach that substantially reduces the computational effort required. This interface, called the Subdomain Modeling Tool (SMT), streamlines the pre- and post-processing requirements of subdomain modeling by allowing modelers to extract regions of interest interactively and by organizing project data on the file system. Software design and implementation issues that make the approach practical, such as a novel range search algorithm, are presented. Descriptions of the overall methodology, software architecture, and performance results are given, along with a case study demonstrating its use.

Defining the Critical Factors in the Architectural Design of Remote Laboratories

In the last decade, we have witnessed a significant proliferation of remote laboratories, unconstrained by temporal or geographical considerations, in all fields of engineering education, thanks to the exponential revolution of digital technologies and computers. In response to these needs, this paper provides a literature review on the commonly encountered hardware and software implementation issues nowadays. It provides a generic model for developing and implementing modern remote laboratories suited for a wide spectrum of application categories and for the next-generation educational systems. This paper addresses all the components and stages of the architectural design, outlining the critical factors to be considered in each of them. The overall goal is to provide guidelines for developers, either providers or consumers, and to facilitate remote laboratories development, implementation, and sharing across universities—seamlessly and efficiently.

Design and analysis of target-sensitive real-time systems

A significant number of real-time control applications include computational activities where the results have to be delivered at precise instants, rather than within a deadline. The performance of such systems significantly degrades if outputs are generated before or after the desired target time. This work presents a general methodology that can be used to design and analyze target-sensitive applications in which the timing parameters of the computational activities are tightly coupled with the physical characteristics of the system to be controlled. For the sake of clarity, the proposed methodology is illustrated through a sample case study used to show how to derive and verify real-time constraints from the mission requirements. Software implementation issues necessary to map the computational activities into tasks running on a real-time kernel are also discussed to identify the kernel mechanisms necessary to enforce timing constraints and analyze the feasibility of the application. A set of experiments are finally presented with the purpose of validating the proposed methodology. Copyright © 2015 John Wiley & Sons, Ltd.

Defuzzification block: New algorithms, and efficient hardware and software implementation issues

The defuzzification is a critical block when implementing a fuzzy inference engine due to different variations and also high computational power demands of defuzzification algorithms. These various methods stand for different cost-accuracy trade-off points. Three new implementation friendly defuzzification algorithms are presented in this paper and compared with a complete set of existing defuzzification methods. Some accuracy analysis simulation results and analytic studies are provided to demonstrate that these methods provide acceptable precision with respect to other existing methods. The software models of the proposed and exiting defuzzification methods are developed under three well-known platforms, Intel's Pentium IV, IBM's PowerPC, and TI's C62 DSP to show that new methods gain much lower execution-time and instruction-count with respect to the most common existing methods. The hardware models of all these methods are also developed and synthesized to demonstrate the superiority of the new methods in terms of area, delay, and power consumption with respect to other methods when implemented in hardware.