Software Product Development Research Articles

Emotions and Perceived Productivity of Software Developers at the Workplace

Emotions are known to impact cognitive skills, thus influencing job performance. This is also true for software development, which requires creativity and problem-solving abilities. In this paper, we report the results of a field study involving professional developers from five different companies. We provide empirical evidence that a link exists between emotions and perceived productivity at the workplace. Furthermore, we present a taxonomy of triggers for developers’ positive and negative emotions, based on the qualitative analysis of participants’ self-reported answers collected through daily experience sampling. Finally, we experiment with a minimal set of non-invasive biometric sensors that we use as input for emotion detection. We found that positive emotional valence, neutral arousal, and high dominance are prevalent. We also found a positive correlation between emotional valence and perceived productivity, with a stronger correlation in the afternoon. Both social and individual breaks emerge as useful for restoring a positive mood. Furthermore, we found that a minimum set of non-invasive biometric sensors can be used as a predictor for emotions, provided that training is performed on an individual basis. While promising, our classifier performance is not yet robust enough for practical usage. Further data collection is required to strengthen the classifier, by also implementing individual fine-tuning of emotion models.

Building Better Digital Twins for Production Systems by Incorporating Environmental Related Functions—Literature Analysis and Determining Alternatives

The digital twin solution is an industry 4.0 specific tool that has grown in the past decade, stemming from the modelling and simulation approaches that existed before, complemented by new sensor capabilities, cloud processing, big data analytics, and implementation mechanisms. As it is being used mostly in the present by manufacturing companies, the primary focus of the solution is to enhance productivity and reduce costs by optimizing processes and enabling real-time problem-solving, sometimes based on decision-making systems and artificial intelligence. However, as companies are being faced with an increasingly steep list of environmental requirements and regulations, ranging from the classical pollution control and waste recycling to full-scale economic models based on circular economy and transformative carbon dioxide elimination programs, the features of the manufacturing digital twins must also evolve to provide an appropriate answer to these challenges. In this paper, the authors propose a framework for building better digital twins for production systems by incorporating environmental-related functions. The demarches start from analysing existing solutions presented in literature from the point of view of environmental suitability, based on the use of the MoSCoW method for differentiating attributes (into Must have, Should have, Could have, Will not have elements) and determining development alternatives based on the employment of Multi-Criteria Decision Analysis (MCDA) for feature selection, and the TRIZ method (Theory of Inventive Problem-Solving) for application guidelines. The MCDA was performed within a focus group of nine production specialists from regionally successful sectors. We arrive at the conclusion that environmental-related functions are poorly implemented in the digital twins of the present (although more so in integrated solutions and custom-built applications) and that the development of the proper tools, databases, and interpretation keys should proceed immediately in the fields of production engineering, industrial ecology, and software development to support them.

Prediction of the Structural Properties of Powder Materials by 3D Modeling Methods

This article examines the main problems of modelling spherical (circular) particles. The main method of the initial process of filling lobules using the Cauchy and Reynolds problem is substantiated. An image of an object-oriented complex of free fall of a spherical particle and their many non-collision spheres is presented. Based on the obtained research results, the main parameters of the process of filling particles of heterogeneous materials. An example of visualization of the developed software product for filling material particles is given, taking into account a number of cross-sections of a cylindrical hopper in height. A histogram of the distribution of material particles from porosity over the volume of a cylindrical hopper is also constructed.

DIGITAL TECHNOLOGIES AS MODERN INSTRUMENTS OF THE MANAGEMENT SYSTEM IN THE ENTERPRISES OF THE HOSPITALITY INDUSTRY

The intensive pace of digital transformation creates the need to respond quickly to changes in needs, interests, tools, which are preferred by hospitality customers. From the point of view of business efficiency, digitalization is focused on integrating artificial intelligence, software products into the production process and the process of effective management decisions to solve current situations and solve problems based on ready-made decision algorithms based on real performance indicators. By adapting software resources to the work of enterprises and ensuring not only our own economic security, but above all, we form an unprecedented value to the consumer, assessing their needs and personally meeting their requirements, standards and wishes. This approach helps to increase the level of competitiveness of the enterprise in the market, which is extremely important especially for the hospitality industry, where you need a personal approach to each client. Often, hospitality companies carry out several activities, and the management of the entire production process should be organized in a single technological cycle, for the convenience of the client. It is with the help of software products that it becomes possible to combine completely different technological processes of separate divisions or departments of the enterprise into a single system with the ability to differentiate each type of work and evaluate the results of their activities and individual business processes. Modern software products make it possible to ensure the synergistic effect of the work of separate structural units or even branches of companies. The rapid development of software products encourages companies to move to an innovative type of development, trying to be the first to adapt new tools to their own business. By linking all systems into a single mechanism, we have real-time analytical data that allows us to: track the strengths and weaknesses of the business; understand the economics of the whole process of providing services and be able to quickly correct situations; focus on those channels from which users are most converted to real customers. Therefore, it is important to explore the basic tools of digital technologies that can be effectively used in the process of doing business and increase its competitiveness.

Visibility Matrix: Efficient User Interface Modelling for Low-Code Development Platforms

In this paper, we introduce the idea of the ‘visibility matrix’ for automated data entry form generation in low-code development platforms. We then focus on the problem of software development productivity in the area of automated software generation as the main factor of the Industry 4.0 concept in the area of business information. In our study, two different approaches to user interface development in a business process management low-code platform were evaluated. The first, the multi-form model, assumes that input forms are prepared separately for each user task in the business process being automated. The second approach, the single-form model, assumes that there is one global input form for every task in the business process. Since users have access to different data in different process tasks, it is necessary to prepare the visibility matrix to define which data are relevant to which tasks. The experiments presented in this paper help to answer the following question: which approach yields better results in terms of productivity, which is measured as costs and time required to prepare the application? Several dozen real business processes were analysed to examine the properties of their visibility matrix. Additionally, the real project team members were evaluated to determine their productivity. Then, the productivity parameters were calculated for real business processes and real project teams. The results show which approach is better suited for real-world business process development.

Application of Chaotic Increasing Linear Inertia Weight and Diversity Improved Particle Swarm Optimization to Predict Accurate Software Cost Estimation

Nowadays usage of software products is increases exponential in different areas in society, accordingly, the development of software products as well increases by the software organizations, but they are unable to focus to predict effective techniques for planning resources, reliable design, and estimation of time, budget, and high quality at the preliminary phase of the development of the product lifecycle. Consequently, it delivered improper software products. Hence, a customer loses the money, time, and not belief on the company as well as effort of teamwork will be lost. We need an efficient and effective accurate effort estimation procedure. In the past, several authors have introduced different methods for effort of estimation of the software. Particle Swarm Optimization is a most popular optimization technique. Maintaining diversity in particle swarm optimization is the main challenging one and in this paper, we propose chaotic linear increasing inertia weight and diversity improved mechanism to enhance the diversity. Seven standard data sets were employed to analyze of performance of the proposed technique, and it is outperformed compared to the previous techniques.

Application of Cloud Service-Oriented Heterogeneous Execution Scheduling and VR Technology in Dance Video Teaching

Cloud computing is the result of the revolution in information technology and a massively complex computing system. As a model for business computing, cloud computing aims to facilitate resource sharing and collaborative work, meet the service needs of users, and generate revenue for cloud service providers. How to reasonably allocate cloud resources, efficiently manage and schedule massive application tasks in real time, reduce the cost of users, and increase the income of cloud service providers on the basis of ensuring the load balance of the cloud computing system and enhancing the utilization of cloud resources is, therefore, one of the research hotspots in the current cloud computing environment. Simultaneously, with the rapid development of human motion simulation and virtual reality technologies, the natural cooperation between humans and computers has become the primary focus of computer science research. The motion capture system is able to track, detect, capture, and record real-time human motion. By analyzing the captured three-dimensional data, we can determine the various characteristics of human motion posture at various times. Due to the potential research and practical value of motion capture technology, it is predominantly used in cutting-edge fields such as animation video production, rehabilitation medicine, sports training, and game software development, thereby effectively realizing the connection between the three-dimensional world and the real world. However, the combination of motion capture technology and educational activities is not universally applicable. This research proposes an approach to dance posture analysis based on matching feature vectors, which can be applied to dance teaching and significantly improves the quality of education and teaching activities. For the purpose of this study, it will be determined whether the combination of dance instruction-based motion capture technology and education is effective and feasible.

СОСТАВЛЕНИЕ БЭКЛОГА ПРОГРАММНОГО ПРОДУКТА КАК СПОСОБ ПОДГОТОВКИ БУДУЩЕГО ИТ-СПЕЦИАЛИСТА К ПРОФЕССИОНАЛЬНОЙ ДЕЯТЕЛЬНОСТИ

According to the agile software development methodology, the ability to work in a team for developing software products is relevant for a future IT specialist. The possibility of organizing the activities of future IT specialists using the technology of compiling a backlog related to the Scrum methodology is discussed. An algorithm for preparing students for compiling a project backlog is proposed, including the stages at which students are divided into teams, specify the product backlog, prioritize tasks in sprint backlogs, develop and present versions of the software product, and refl on the results obtained.

A qualitative study of developers' discussions of their problems and joys during the early COVID-19 months.

Many software developers started to work from home on a short notice during the early periods of COVID-19. A number of previous papers have studied the wellbeing and productivity of software developers during COVID-19. The studies mainly use surveys based on predefined questionnaires. In this paper, we investigate the problems and joys that software developers experienced during the early months of COVID-19 by analyzing their discussions in online forum devRant, where discussions can be open and not bound by predefined survey questionnaires. The devRant platform is designed for developers to share their joys and frustrations of life. We manually analyze 825 devRant posts between January and April 12, 2020 that developers created to discuss their situation during COVID-19. WHO declared COVID-19 as pandemic on March 11, 2020. As such, our data offers us insights in the early months of COVID-19. We manually label each post along two dimensions: the topics of the discussion and the expressed sentiment polarity (positive, negative, neutral). We observed 19 topics that we group into six categories: Workplace & Professional aspects, Personal & Family well-being, Technical Aspects, Lockdown preparedness, Financial concerns, and Societal and Educational concerns. Around 49% of the discussions are negative and 26% are positive. We find evidence of developers’ struggles with lack of documentation to work remotely and with their loneliness while working from home. We find stories of their job loss with little or no savings to fallback to. The analysis of developer discussions in the early months of a pandemic will help various stakeholders (e.g., software companies) make important decision early to alleviate developer problems if such a pandemic or similar emergency situation occurs in near future. Software engineering research can make further efforts to develop automated tools for remote work (e.g., automated documentation).

Research of the environmental temperature influence on the horizontal displacements of the Dnieper hydroelectric station dam (according to GNSS measurements)

Abstract The paper studies the relationship between the ambient temperature change and the horizontal displacements on control points of the Dnieper Hydroelectric Station dam from 2016 to 2020. A specially developed software product analyzed the GNSS time series of measurements pre-processed by the GeoMoS system to determine the parameters of seasonal displacements and their relationship with seasonal changes in air temperature. The research established that the influence of ambient temperature in the absence of significant changes in the water level in the upper reservoir determines the cyclicity of dam deformations. It is established that the projections of velocity vectors of reference points in the ETRF-2014 system for the studied period do not exceed the absolute value of 3 mm/month. The directions of the horizontal displacement vectors in the first half of each year are opposite to the directions recorded in the second half. In the first half of the year, the dam’s body shifts towards the reservoir, while in the second half year period, it shifts-backwards. According to the three-year GNSS monitoring of the Dnieper Hydroelectric Station dam, the amplitude of semi-annual horizontal oscillations of the control points relative to the dam axis is from -9.5 to +8 mm. In winter and summer, the horizontal displacements increase from the edges of the dam to its central part, and the amplitudes of the horizontal displacements move vice versa. The obtained data establish a linear analytical relationship between the average temperature and the horizontal displacements of the GNSS control points.

Do You Feel It? The Relationship Between the Perceived Team Climate for Innovations and the Experience of Flow and Worry

This paper examines the relationship between the perceived team climate for innovations and the experience of flow and worry and the moderating effect of team size. The research contains a multi-organization dataset with 323 software product development team members. The results show that the perceived climate for innovation is significantly positively related to the experience of flow and worry. However, the findings did not support the moderation with no differences in working in smaller or larger teams. This study indicates that the perceived climate for innovations positively relates to positive and negative individual effects without the importance of team size.

Mapping Processing Elements of Custom Virtual CGRAs onto Reconfigurable Partitions

FPGAs can provide application-specific acceleration for computationally demanding tasks. However, they are rarely considered general-purpose platforms due to low productivity of software development and long reconfiguration time. These problems can be mitigated by implementing a coarser overlay atop the FPGA fabric. Combining this approach with partial reconfiguration allows for the modification of individual processing elements (PEs) of the virtual architecture without altering the rest of the system. Module relocation can be used to share implementation details between functionally equivalent PEs that use identical sets of resources, thus eliminating redundant placement and routing runs. Proper floorplanning is crucial for virtual Coarse-Grained Reconfigurable Architectures (CGRAs) with relocatable PEs considering their tendency to use nearest-neighbor connection patterns. It requires solving two problems—finding identical regions in the FPGA fabric and assigning individual partitions to certain locations. This article presents minor improvements of a state-of-the-art solution for the first and proposes a novel technique for solving the other. The proposed automated floorplanner uses modified breadth-first search with direction-based penalties to create initial floorplan consistent with geometry of logical array, then improves the result with 2-opt local optimization. Compared to simulated annealing solutions, the proposed approach allows for the reduction in the floorplanning time by two to three orders of magnitude without compromising the quality of the results.

MERIDIAN open-source software for deep learning-based acoustic data analysis

Deep neural networks have the potential to transform our approach to developing acoustic detection and classification models, enabling acousticians to develop or re-purpose such models through a fully data-driven approach requiring minimal knowledge of signal processing, algorithm design, and programming. However, open-source software to facilitate this data-driven workflow is currently lacking. MERIDIAN is working towards filling this gap through the development of several open-source software products, including the Python package Ketos and the MAIPL (Marine AI PLatform) suite of web applications. While Ketos provides a high-level programming interface for training deep neural networks at detecting and classifying sounds, MAIPL is a modular cloud computing service that supports the full model-development workflow. In this contribution, an overview of Ketos and MAIPL will be given and their functionalities will be demonstrated through their application to the HALLO (Humans and ALgorithms Listening for Orcas) project. We highlight one of the MAIPL tools, the MAIPL-Annotator, which provides a user-friendly interface for collaboratively annotating sound samples and validating model predictions. Future developments will also be described, highlighting new MAIPL applications under development such as the MAIPL-Adapter, a tool for adapting acoustic deep learning models to new acoustic environments.

The impact of the software architecture on the developer productivity

Abstract Distinct technological trends seriously influence the modern software architectures. In this paper, four different software architectures and framework combinations were generally compared. The basis for the analysis is the developer's productivity. In a 3 year-long research and development project, a real-world telemedicine application was efficiently implemented four times with various software architectures and architectural patterns. More than 5,000 person-hours were spent on carrying out them. At present, a unique dataset is available, which provides the opportunity to compare the cost of spent person-hours in different approaches. The goal of this research is to describe the measurement approach, the dataset and the applied architectures considering the software developer's productivity.

Management of Students' Self-organization in the Context of the Development of Information and Analytical Skills in the Digital Environment

The article reveals the features of the implementation of the developed software product in order to develop the level of future teachers’ self-organization in the digital environment. The purpose of this article is to justify the need to implement the developed software product aimed at developing the level of future teachers’ self-organization and the formation of information and analytical skills in the digital environment. In order to assess the formation of information and analytical skills, we developed a methodology based on performing operations with text, where each skill was assessed on a scale from 0 to 2. As a result of diagnostics, we found that the majority of students are dominated by a low and medium level of formation of information and analytical skills. As a means of self-organization management, a description of the designed software module was presented, which contributes to an increase in the level of development of information and analytical skills. The authors suggested that the introduction of the developed software product into the digital environment will be aimed at increasing the level of development of information and analytical skills of future teachers. According to the results of the work, the conclusion was made about the effectiveness of the process of forming information and analytical skills of students by using an integrated approach to the selection and compilation of tasks and building a unified digital environment.

Optimized Feature Selection in Software Product Lines using Discrete Bat Algorithm

Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population.

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects.

Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students’ skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development.

VISUAL SIMULATION TECHNOLOGY FOR PASSENGER FLOWS IN THE PUBLIC TRANSPORT FIELD AT SMART СITY

Context. Today, the problem of visual simulation of passenger flow in public transport is essential in creating information systems for the development of modern Smart City. In Industry 4.0, it is crucial to develop technologies, means, and tools for implementing a single self-regulatory intelligent data exchange system in the provision of appropriate passenger transportation services in public transport. Today the following is essential: to visually display problem areas on routes in Smart City; to form and identify the main stops in time sections with the largest passenger exchange; to create proposals on the need to modernise routes taking into account the increase in public transport congestion in certain areas of Smart City, and to obtain results of passenger flow forecasting when making appropriate changes based on machine learning methods.
 Objective of the study is to develop a technology for visual simulation of passenger traffic in the field of public transport to improve the quality of passenger services in Smart City.
 Method. They have improved the simulation model for calculating passenger flow when changing the number of rolling stock on the route, in contrast to the known, added forecasting based on the developed neural network. The mechanism of visual simulation of passenger flows using GoogleMaps maps and dynamic movement on them with control of simulation display speed has been improved. A neural network with fully connected layers utilising an optimisation algorithm with an adaptive level of learning Adam to predict the flow of passengers between stops for a certain period of the day is proposed. Criteria for detailing passenger flows on urban routes are defined, including general indicators of the ratio of passenger traffic at a specific stop to the current period of the day. When designing the intelligent system, changing the capacity of public transport rolling stock in Smart City was further developed. Unlike the known ones, the available vehicles limit the change of power. The method of calculating a set of indicators of passenger traffic at stops and races, taking into account different local schedules and the specifics of transport on individual routes, has undergone further development.
 Results. An intelligent system of visual modelling of passenger traffic based on a neural network and machine learning has been developed, allowing optimising passenger traffic by public transport in Smart City. This data presentation makes it possible to assess the profitability of adding a new vehicle to the route or adjusting the schedule of other cars to cover the loaded areas during peak hours better. The well-known standard of public transport data presentation – GTFS is used for the operation of the software. It allows you to adapt the developed software product to the universal, rather than specific to a particular city or country. It was provided with a comparison of the obtained results on a data set of trolleybus routes (about 2000 records, collected based on experimental marketing research) in Lviv (Ukraine) to form a forecast of changes in passenger flow on certain sections at different times.
 Conclusions. It was found that the passenger flows predicted by the neural network in comparison with the actual ones lead to their growth by an average of 28% in critical races at rush hour. These results allow us to justify adding a schedule of a new vehicle for better coverage of loaded areas during peak hours. A comparison of changes in passenger traffic distributed by races during the day from 19:00 to 20:00, according to actual data and after the operation of the neural network indicates an increase in their average 70% of races that were predicted, which will allow a reasonable decision to launch additional transport on appropriate routes.

Inter-team communication in large-scale co-located software engineering: a case study

Large-scale software engineering is a collaborative effort where teams need to communicate to develop software products. Managers face the challenge of how to organise work to facilitate necessary communication between teams and individuals. This includes a range of decisions from distributing work over teams located in multiple buildings and sites, through work processes and tools for coordinating work, to softer issues including ensuring well-functioning teams. In this case study, we focus on inter-team communication by considering geographical, cognitive and psychological distances between teams, and factors and strategies that can affect this communication. Data was collected for ten test teams within a large development organisation, in two main phases: (1) measuring cognitive and psychological distance between teams using interactive posters, and (2) five focus group sessions where the obtained distance measurements were discussed. We present ten factors and five strategies, and how these relate to inter-team communication. We see three types of arenas that facilitate inter-team communication, namely physical, virtual and organisational arenas. Our findings can support managers in assessing and improving communication within large development organisations. In addition, the findings can provide insights into factors that may explain the challenges of scaling development organisations, in particular agile organisations that place a large emphasis on direct communication over written documentation.

АНАЛІЗ ЦИФРОВИХ ТЕХНОЛОГІЙ ФОНДОВОЇ БІРЖОВОЇ ТОРГІВЛІ В УКРАЇНІ

Activation of investment activity for the recovery of the economy after the corona crisis requires the development of stock exchange trading as part of trading in state securities, municipal securities, corporate securities, etc. Institutional, organizational and technical conditions have not been created for the development of stock exchange trading in Ukraine. The purpose of the article is to study the current state of digital technologies of stock exchange trading in Ukraine. An analysis of stock exchange trading in Ukraine was carried out based on the analysis of general activity on the stock market, analysis of stock market segments and activities of operators of organized markets. In 2014–2017, the stock market of Ukraine was in a state of stagnation due to a decrease in trading volumes. The recovery of the activity of stock market participants took place in 2018–2021. Activity on the stock market of Ukraine is ensured mainly in the secondary segment. The share of the secondary market in the stock market of Ukraine in 2014–2021 was more than 97%. The digital technologies of the stock exchange "Perspektiva" (Electronic trading system, BIT eTrade Mail, BIT eTrade, BIT eTrade Future, BIT eTrade IPO, BIT ePayment, BIT eReport, etc.) are characterized. Further restoration of stock exchange trading in Ukraine and use of the potential of the stock market for the needs of economic recovery is possible only under certain conditions. There are an increase in the number of issuers of debt securities and equity securities, publicity of information about the stock market and stock market participants, protection of stock market participants, improvement of financial literacy of the population, improvement of digital technologies of stock exchange trading. It is possible to improve digital technologies and activate the digitization of stock exchange trading in Ukraine through the development of software products and mobile applications for all stock trading participants, the use of artificial intelligence, Big Data for the formation and organization of information on the stock market, the integration of digital technologies of stock exchange trading with state registers and open data services.