Software Representation Research Articles

Contract-based Testing Using the Ontology Approach

This article analyzes the use of contract testing to verify the compatibility of two components, specifically web servers that use an Application Programming Interface (API) for data transmission. Additionally, the article includes a comparison of APIs and contract tests and describes cases where the latter have an advantage. A contract structure for contract testing is described. The article contains a description of the ontological approach for comparing knowledge about the business systems of the provider, in the form of ontologies, with the software representation of the consumer system, stored in the form of classes. A structure of the object that supplements the contract by providing it with business logic level checks is developed. Basic predicates that form the basis of this method are proposed and described. The use of the mapping field allows for the use of thesauruses or dictionaries for automated replacement of concepts when using synonym words. An example is provided that demonstrates the operation of this approach, namely: a simplified representation of the provider system’s concepts, a simplified structure of the consumer system’s classes, an example of an API and a contract for it, an extension using the developed approach, and the result of test execution.

Uncovering Malware Families Using Convolutional Neural Networks (CNN)

Malware attacks pose significant cyber threats, with a rising number of vulnerability reports in security communities due to the continual introduction of mutations by malware programmers to evade detection. One of the most attractive targets which attacked by malware is the organization emails system. Malware’s mutations within the malware family, has complicating the development of effective machine learning-based malware analysis and classification methods. To answer this challenge, this research uses an agnostic deep learning solution inspired by ImageNet's success, which efficiently classifies malware into families by analyzing visual representations of malicious software as greyscale images using a Convolutional Neural Network (CNN). The Malwizard is a flexible Python tool suitable for both organizations and end-users enabling automated and rapid malware analysis within email system. Malwizard could be use as an Outlook Email’s add-in and an API service for SOAR platforms. The study evaluates this novel approach using the Microsoft Classification Challenge dataset, where image representations are encrypted to address privacy concerns. Experimental results show that the proposed approach performs comparably to the best existing model on plain text data, accomplishing the task in one-third of the time. For the encrypted dataset, adjustments to classical techniques are necessary for improved efficiency.

An open GIS based 3D simulation software to predict cooling tower drift diffusion

This paper developed XJCT-3D, a simulation software for cooling tower wet plume dispersion. By coupling it with the Open GIS component Dotspatial, we have achieved geospatial visual representation of the calculation results, which has solved the problems of low calculation efficiency and insufficient visual representation of the traditional CFD software in the calculation of cooling tower wet plume dispersion. In order to verify the validity of the XJCT-3D software simulation results, we have conducted tracer experimental data from the ChalkPoint power plant. XJCT-3D accurately models wet plume deposition during cooling tower operation. From the XJCT-3D calculation results, we have observed that the maximum value of the cooling tower thermal plume wet deposition occurs near 610 m with a maximum value of 6.9E−07 kg/m2 s. This finding suggests that the cooling tower emissions carry a significant load of particles or droplets that have settled on surfaces at this particular altitude. It provides insights into potential environmental and human health impacts and helps in identifying and assessing areas at relatively higher risk of deposition, such as nearby ecosystems, farmland, or urban areas. This information can contribute to the development of effective mitigation strategies and the implementation of appropriate measures to minimize the impact of cooling tower emissions.

Locating community smells in software development processes using higher-order network centralities

Community smells are negative patterns in software development teams’ interactions that impede their ability to successfully create software. Examples are team members working in isolation, lack of communication and collaboration across departments or sub-teams, or areas of the codebase where only a few team members can work on. Current approaches aim to detect community smells by analysing static network representations of software teams’ interaction structures. In doing so, they are insufficient to locate community smells within development processes. Extending beyond the capabilities of traditional social network analysis, we show that higher-order network models provide a robust means of revealing such hidden patterns and complex relationships. To this end, we develop a set of centrality measures based on the MOGen higher-order network model and show their effectiveness in predicting influential nodes using five empirical datasets. We then employ these measures for a comprehensive analysis of a product team at the German IT security company genua GmbH, showcasing our method’s success in identifying and locating community smells. Specifically, we uncover critical community smells in two areas of the team’s development process. Semi-structured interviews with five team members validate our findings: while the team was aware of one community smell and employed measures to address it, it was not aware of the second. This highlights the potential of our approach as a robust tool for identifying and addressing community smells in software development teams. More generally, our work contributes to the social network analysis field with a powerful set of higher-order network centralities that effectively capture community dynamics and indirect relationships.

Digitally Cloning the Physical

The developments of Digital Twin-based solutions is receiving increasing attention from a large community of developers. The concept of the Digital Twin is not anymore confined into the realm of manufacturing. Lots of applications are adopting the approach in different application domains such as smart cities, e-health, logistics, education, and many more. The Digital Twin is a software representation of a corresponding complex physical object. The digital twin is based on two important features that are of the interest for the software and Information Technology community: data and modeling. Data are the central aspect for monitoring and representing the status of a physical object. Having access to well-formed data makes it possible to represent the salient features and characteristics of the physical object. The second major feature is how these data are to be organized and processed in order to reflect the actual status of the physical object.

A Robotic System for Solo Surgery in Flexible Ureterorenoscopy

Urolithiasis is a common disease with increasing prevalence across all ages. A common treatment option for smaller kidney stones is flexible ureterorenoscopy (fURS), where a flexible ureteroscope (FU) is used for stone removal and to inspect the renal collecting system. The handling of the flexible ureteroscope and end effectors (EEs), however, is challenging and requires two surgeons. In this article, we introduce a modular robotic system for endoscope manipulation, which enables solo surgery (SSU) and is adaptable to various hand-held FUs. Both the developed hardware components and the proposed workflow and its representation in software are described. We then present and discuss the results of an initial user study. Finally, we describe subsequent developmental steps towards more extensive testing by clinical staff.

Software for the generalization of the Vehicle Routing Problem

This article presents a software solution to the generalization of the Vehicle Routing Problem. The Vehicle Routing Problem is a logistics task, the purpose of which is to transport goods and services to a given place at a given time in a cost-effective manner. Many versions of the task have developed over the years, which model constantly changing, complicated systems. The article details the software solution of a model that models a general system. The software representation contains the following components: vector describing the order of nodes, the matrix describing the assignment of vehicles-nodes-products, vector describing the assignment of vehicle - recharger stations, vector describing the vehicle-start-end-node assignment, vector describing level, vector describing period, solution description vector. The following optimization algorithms were used in the article: Ant Colony System, Genetic Algorithm, Simulated Annealing and Tabu Search. The article presents the literature of the Vehicle Routing Problem, the software solution of the general system, the applied algorithms and their running results through a case study.

СОЗДАНИЕ И ИНТЕГРАЦИЯ ЦИФРОВОГО ДВОЙНИКА

Introduction. Smart lifecycle services, along with the adoption of other smart manufacturing strategies, show significant potential to increase the productivity and competitiveness of enterprises. There is a clear need for high quality process models and software representations of physical hardware that reflect the evolution of their physical counterparts in particular detail. Digital twin technology can provide fertile ground for the development of IoT based lifecycle applications. This paper proposes a software approach to the process of developing a digital twin. The purpose of this work is to study the methods of creating software frameworks in the context of digital twin technology, as well as to study the process of creating and integrating digital twins and developing our own tool. Existing methods for creating twins are considered and a self developed Python library is described. The work uses the methods of the category of research in action and the philosophy of Agile development. Methods. The choice of methodology in seeking answers to questions that arise when studying methods for improving digital twins mainly falls into the category of research in action. In this paper, research in action is used as a qualitative tool, well suited to situations where the researcher seeks to achieve two different goals, namely: to solve a contemporary problem faced by the organization; contribute to a pool of knowledge that can later be used by other people to solve problems in the same class of problems. Results. Created library supports two modes of data retrieval: using periodic database queries and TCP socket connection. The experiments carried out as part of the work allow us to conclude that the library can already be used as a tool for creating a digital twin and its integration.

Modelling dielectric elastomer circuit networks for soft biomimetics

In order to obtain entirely soft bio-inspired robots, fully soft electronic circuits are needed. Dielectric elastomers (DEs) are electroactive polymers that have demonstrated multifunctionality. The same material can achieve different tasks like actuation, sensing, or energy harvesting. It has been shown that basic logic and memory functions can be realized with similar soft structures by combining multiple DE actuators and DE switches. Thus it would be possible to build, with the same materials and processes, a soft structure that mimics a biological being with all these capabilities. This contribution is focused on the modelling of the aforementioned soft electro-mechanical circuit networks. It is here reported the building process of a comprehensive SIMULINK model including the electro-mechanical behaviour of DE logic units and their interconnections. Conventional models deal with a single aspect of DEs, generating complex finite-element simulations. This contribution, based on a former model for an inverter-based DEO, shows how to integrate these various mathematical models and, with the help of direct measurements, create a software representation of DE circuit networks. This work is intended to demonstrate the validity of a recently introduced model and apply it to more complex circuit networks that have a higher number of components. Since, at the present state, the building processes are by hand, adding components generates more variability due to sample-to-sample variation and human error. Despite this, the model still shows a qualitatively good prediction of the devices’ behaviour. Furthermore, the introduction of new materials and automatic processes will help to reduce this variability, allowing the model to reach even better performance.

Improving energy efficiency while preserving historic buildings with digital twins and artificial intelligence

This study proposes a digitalization framework for historic buildings. In this framework, advanced techniques, like Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are utilized to create digital twins for historic buildings. A digital twin is a software representation of a physical object. This study uses digital twins to protect, predict, and optimize through analytics of real-time and historical data of selected features. Heterogeneous data of historic buildings, such as indoor environment, energy consumption metering, and outdoor climate, are collected with proper sensors or retrieved from other data sources. Then, these data are periodically uploaded and stored in the database of the cloud platform. Based on these data, AI models are trained through appropriate machine learning algorithms to monitor historic buildings, predict energy consumption, and control energy-consuming equipment autonomously to reach the balance of energy efficiency, building conservation, and human comfort. The cloud-based characteristic of our digitalization framework makes the digital twins developed in this study easy to be transplanted to many other historic buildings in Sweden and other countries.

Novel approach to transform UML Sequence diagram to Activity diagram

Unified Modelling Language (UML) is currently accepted as a defacto standard language for modeling the software in the software industry. It will allow to implement object oriented concepts to model the software system. It provides a complete pictographic representation of software. Broadly these UML diagrams are classified into two groups viz. Structural diagrams and Behavioral diagrams. The sequence diagrams and Activity diagrams belongs to the second group i.e. behavioral diagrams. The sequence diagram represents the sequence of messages flowing from one object to another and activity diagram represents the flow of activities one after the other in a system. In this paper, we are proposing an automated tool which transforms the sequence diagram (which is represented in the table format) into activity diagram. The sequence diagram which is represented in the three column table called sequence table comprises various components of sequence diagram like objects, interactions, messages, alternations, iterations, loops, etc. The proposed tool reads the sequence table and converts the entire table components into the equivalent Activity table. Further the tool reads the activity table and then transforms to its equivalent activity diagram.

Spliced images detection by using Viola-Jones algorithms method

Photographs are measured to be a useful document that is used to stored life proceedings. The new progress in the representation of image expurgation software cause image operation. The additional and familiar image splicing, will not disappear with any illustration sign that having it interfered. Consequently, it turns complicated to identify whether the images are genuine or not. There are mostly three types of image forgery, these are image splicing, copy-move, and image retouching. There are dissimilar techniques accessible to ensure the dependability of the image. This manuscript proposed a Viola-Jones algorithms technique to detect the splice in images consisting of a human being. The primary stage, detected all the facts presented in the image then it is distorted addicted to dissimilar color spaces. Then it is used illuminant maps of every color space is achieved. Extracted the special feature for example quality of color and a number of the image classes are measured. The SVM classifier is used to detect whether the images are fake or real.

System Error Estimate using Combination of Classification and Optimization Technique

The representation of software must produce flawless significances without any inadequacies. Software imperfection evaluations scheme determines defective mechanisms in software. The eventual creation would have minor or negligible shortcomings to harvest great eminence software. Software quality metrics are a division of software metrics that spotlight the quality aspects of the product. The software flaw prediction system helps in the early discovery of flaws and contributes to talented removal and producing a quality software system through numerous metrics. The aim of the paper was to show how static model of data mining is used to extract defects and the PSO algorithm. Another aim of the research was to develop an optimized software flaw prophecy system on data mining techniques namely Association Rule mining, Decision Tree, Naive Bayes and Classification integrated with Particle Swarm Optimization technique. The proposed software flaw prediction system is deliberated through Data Mining techniques with Particle Swarm Optimization algorithm has been verified and compared the results. This proposed system is very useful to identify the relationships between the quality metrics and the potential defective modules. The optimized data mining systems have pragmatic perfect prediction of these defective modules. In the future, optimized data mining systems can be improved by the use of different platforms and particularly by improving data mining using PSO algorithms. It is necessary to develop algorithms that can identify faults in advance, which will minimize costs and promote the quality of developed software systems. Future optimized data mining systems will improve the relationship between quality metrics and the potential defective modules, which will lead to improved performances, productivity and lower operation costs.

A METHOD OF REDUCING THE ERROR IN DETERMINING THE ANGULAR DISPLACEMENTS WHEN USING INDUCTIVE SENSORS

Goal. Representation of a special mathematical software for determining the angular displacements of the rotor of the induction angle sensor – resolver (rotating transformer) for applications in which the speed of the sensor's rotor is close to zero. As well as performing its experimental verification. Methodology. The presented method is based on the determination of the phase shift angle of the output signals of the induction sensor, which is determined by comparing the obtained arrangements of signal values with a circular discrete convolution in order to achieve the most precise approximation of the obtained signal values to cosine and sine. The conversion of orthogonal components to an angle is based on the use of a digital phase detector which is use of a software comparator and inverse trigonometric functions. Results. Based on the obtained results of mathematical modeling and experimental research, the characteristic dependencies of the angle of rotation of the rotor of the induction sensor relative to its stator, the nature of which is linear, were obtained. In addition, the estimation of measurement errors of angular displacements is carried out that occur when defining such angles by the method offered. The obtained results of the computer simulation taking into account the high signal noise, as well as the results of experimental investigations, confirm the high precision of this method and the fact that it can be used in systems where high positioning accuracy is required and the speed of the sensor shaft is close to zero. Originality. This article introduces, for the first time, special mathematical software for a new method of determining the angular displacements of the rotor of an induction sensor, which is based on the determination of the orthogonal components of the signal in combination with the use of a circular discrete convolution in the determination of the phase shift angle of the induction sensor signals. Practical meaning. The proposed method does not require the use of demodulators, counters and quadrant tables associated with conventional methods for determining the phase shift of signals. The presented method can be used to measure the full range of 0-2p angular displacements in real time, is simple and can be easily implemented using digital electronic circuitry.

AUTOMATED IDENTIFICATION OF AN OPERATOR ANTICIPATION ON MARINE TRANSPORT

Context. The article discusses approaches to anticipation identification being an essential part of the decision-making process done by the operator by using the example of a sea captain in ergatic systems of critical infrastructures in the sea transport management. The mentioned above aspect of anticipation of operators can be regarded as being a complex form of human-machine interaction and, certainly, claims for further elaboration of information and tools to be used. Objective. The way to approach development is taken as being based on an information analysis of the full range of trajectories of decision-making by operators at the time of performing complex multi-stage actions. These items are rooting out of their adopted strategy of human-machine interaction. Besides, it leads to the formation of a metric being able to algorithmically represent the enormous number of variants. It can be done taking into account conditions of combinatorial representation in terms of the geometric theory of groups on the Cayley graph. Method. Being a part of the approach elaboration the having been obtained during the analysis of the database of navigation simulators mathematical model of experimental data collecting and processing succeeded to be constructed. To confirm the formalalgorithmic approach a simulation was challenged to be carried out helping to form the trajectory of the operator’s decision making in critical situations. It was felicitously performed basing on the three-factor ERO-AEA-EAPI model. Thus, the algebraic and software representation of the metric decision space is noticed to uncover approximate complex human-machine interactions in uncertain environments. As a result, the converting process of data of the main subject of critical infrastructure (i.e. the operator) into knowledge is able to be coped with. In addition, factors possible to be gauged in the proposed metric are able to be uncovered. Results. In order to carry out the feasibility assessment of the developed approach as well as formal-algorithmic ones, an experiment was performed by using the Navi Trainer 5000 navigation simulator (NTPRO 5000). During having one of the most troublesome operations i.e. mooring we wanted the server data to be analyzed. As a result, data about anticipation being shaped as triangular constructs in the quasi-isometric space of Cayley graph is reported to have been obtained. The automated neural networks being used for result obtaining led to delivering of the possibility to get multiple data regression and to analyze the relationships of many independent variables. It is considered to be clear evidence due to having found out results of scattering and reliability diagrams. Conclusions. The having been presented in the investigations formal-algorithmic approach together with the developed software tools and the approaches of converting data into knowledge about operator anticipation are said to embrace the possibility to classify and to identify individual decision-making strategies when managing a vessel and to predict the likelihood of poor consequences. With regarding to the cogency of the proposed approach and models these issues happen to have been successfully justified by means of the automated processing of experimental data.

Software Operational Profile vs. Test Profile: Towards a Better Software Testing Strategy

Software Operational Profile (SOP) is a software specification based on how users use the software. This specification corresponds to a quantitative representation of software that identifies the most used software parts. As software reliability depends on the context in which users operate the software, the SOP is used in software reliability engineering. However, there are evidences of a misalignment between the software tested parts and SOP. Therefore, this paper investigates a possible misalignment between SOP and the tested software parts to obtain, based on experimental data, more evidence of this misalignment. We performed an exploratory study composed of four activities to verify: a) whether there are significant variations in how users operate the software; b) whether there is a misalignment between SOP and the tested software parts; c) if failures occur in untested SOP parts in case of misalignment; d) in case of misalignment between SOP and untested software parts, whether a test strategy based on the amplification of the existent test set with additional test data generated automatically, can contribute to reduce the misalignment. We collected data form four software while users were operating them. We analyzed this collected data in an attempt to reach the goals of this work. To evaluate the originality of this research, we performed a Literature Systematic Review (SLR) and presented its conclusions. The obtained results evidence that there are significant variations in how users operate the software and also that there is a misalignment between SOP and the tested software parts when we evaluated the four software mentioned above. There are also indications of the occurrence of failures in the untested SOP parts. Although the test strategy mentioned above has reduced the possible misalignment, the test strategy is not enough to avoid it, thus denoting the need of specifics test strategies using SOP as a test criterion. These results indicate that SOP becomes relevant not only to software reliability engineering but also to contribute to testing activities, regardless of the adopted strategy.

Algorithm selection for software validation based on graph kernels

Algorithm selection is the task of choosing an algorithm from a given set of candidate algorithms when faced with a particular problem instance. Algorithm selection via machine learning (ML) has recently been successfully applied for various problem classes, including computationally hard problems such as SAT. In this paper, we study algorithm selection for software validation, i.e., the task of choosing a software validation tool for a given validation instance. A validation instance consists of a program plus properties to be checked on it. The application of machine learning techniques to this task first of all requires an appropriate representation of software. To this end,we propose a dedicated kernel function, which compares two programs in terms of their similarity, thus making the algorithm selection task amenable to kernel-based machine learning methods. Our kernel operates on a graph representation of source code mixing elements of control-flow and program-dependence graphs with abstract syntax trees.Thus, given two such representations as input, the kernel function yields a real-valued score that can be interpreted as a degree of similarity. We experimentally evaluate our kernel in two learning scenarios, namely a classification and a ranking problem: (1) selecting between a verification and a testing tool for bug finding (i.e., property violation), and (2) ranking several verification tools,from presumably best to worst, for property proving. The evaluation, which is based on data sets from the annual software verification competition SV-COMP, demonstrates our kernel to generalize well and to achieve rather high prediction accuracy, both for the classification and the ranking task.

Application of Machine Training to Search Vulnerabilities in the Software Code

The article considers the possibility of using artificial intelligence in the field of information security. For this, the following domain contradiction was highlighted: sharpening of detection algorithms for specific vulnerabilities in VS code, constant modification of the vulnerability code. To resolve the contradiction, an object is considered – software with vulnerabilities in the process of its development, for the subject – ways of intellectual analysis of its characteristics. A hypothetical solution is proposed through the use of Machine Learning Technology to identify vulnerabilities in software, using a huge amount of accumulated knowledge. Also, possible signs of software representations used for the operation of the Technology are given.

Software structure evolution and relation to subgraph defectiveness

Network analysis has been successfully applied in software engineering to understand structural effects in the software. System software is represented as a network graph, and network metrics are used to analyse system quality. This study is motivated by a previous study, which represents the software structure as three-node subgraphs and empirically identifies that software structure continuously evolves over system releases. Here, the authors extend the previous study to analyse the relation of structural evolution and the defectiveness of subgraphs in the software network graph. This study investigates the behaviour of subgraph defects through software evolution and their impact on system defectiveness. Statistical methods were used to study subgraph defectiveness across versions of the systems and across subgraph types. The authors conclude that software versions have similar behaviours in terms of average subgraph type defectiveness and subgraph frequency distributions. However, different subgraph types have different defectiveness distributions. Based on these conclusions, the authors motivate the use of subgraph-based software representation in defect predictions and software modelling. These promising findings contribute to the further development of the software engineering discipline and help software developers and quality management in terms of better modelling and focusing their testing efforts within the code structure represented by subgraphs.

IFVM Bridge: A Model Driven IFML Execution

Graphical user interfaces (GUIs) present a powerful part of software systems that allows a simplified assimilation and manipulation by users throw visual objects such as text, image and button. however, with the increasing complexity of GUIs and the diversity of their interaction mode required by users to access information anywhere and anytime, the need for designing efficient and more adaptive user interfaces has become a necessity. Therefore, UIs researchers have attempted to address these issues by designing user interfaces at a high level of abstraction to separate GUI’s specification from its implementation. Besides, the OMG (Object Management Group) adopted the Interaction Flow Modeling Language (IFML) as a standard in March 2013 for this purpose. In this paper, we present a new model driven development approach to efficiently execute the abstract representation of software’s front-end with focus on navigation between the views. We introduce a IFML virtual machine IFVM which executes user interfaces by passing from IFML models to be translated into an intermediate bytecode representation proposed as the instruction set of IFVM virtual machine.