Software Architecture Research Articles

Quality attributes of software architecture in IoT-based agricultural systems

Software architecture forms the cornerstone for achieving and ensuring various software quality attributes. It encompasses the collected requirements of the product, serving as a blueprint that delineates quality features for all project stakeholders, along with methods for measurement and control. Despite the significant increase in IoT-based agricultural systems, there is a dearth of studies on the quality elements of their software architecture. To address this need, this study offers an overview of components and services tailored to address specific quality attributes pertinent to agriculture systems. It identifies, investigates, and presents quality attributes influencing the design of software architecture for IoT-based agriculture systems. This paper identified and discussed several quality attributes, including performance, scalability, flexibility, interoperability, productivity, extensibility, and security, and mapped them to corresponding components of the IoT-based agriculture software architecture. Also, several issues were identified and discussed for the software architecture quality of IoT-based agriculture systems, such as real-time processing and interoperability due to the various devices and protocols utilized in these systems. The findings of this study offer valuable insights for developing, executing, and refining IoT-based agricultural systems to fulfill the changing requirements of the agriculture industry.

Sustainable environmental design: evaluating the integration of sustainable knowledge in Saudi Arabian architectural programs

By aligning Saudi Vision 2030 with the UN's SDGs, this study highlights the significant role of integrating sustainable environmental design knowledge into architectural education in Saudi Arabia. Viewing this integration as a moral obligation, it emphasizes equipping future architects with the skills to create environmentally responsible and resource-efficient buildings, fostering a sustainable and resilient built environment. This paper aims to examine the degree of integration of sustainability knowledge and assess the structural models incorporated into the architectural programs of six Saudi Arabian universities. Adopting a quantitative approach with qualitative insights, this research focuses on universities certified by the National Architectural Accrediting Board (NAAB) or accredited by recognized national and international bodies. This study is limited to the meso-level (curricular management) and includes structural models, course descriptions, learning outcomes, course sequences, and alignment with program visions and missions. A comprehensive review was conducted on all courses related to sustainability, collecting data from program websites. The main variables analyzed included sustainable environmental knowledge, total contact hours dedicated to sustainability courses, distribution across various course types, and patterns over a 5-year period. The units of analysis encompass program plans, vision and mission statements, learning outcomes, course syllabi, and descriptions. The findings reveal significant disparities in sustainability emphasis among programs, with Imam Abdul Rahman Al-Faisal University dedicating 42% of contact hours to sustainability, compared to 25 to 11% at other universities. This study highlights the need for integrating sustainability across design studio levels to provide students with practical skills across disciplines. Most programs use a “linear/parallel” or “partially integrated” model, requiring sustainability principles in graduation projects. However, elective sustainability courses are unpopular and do not adequately address curriculum gaps. This study highlights the necessity of further investigating the effectiveness of incorporating sustainability knowledge in architectural programs. Exploring specific areas for future research to enhance the integration of sustainability in architectural education is also recommended.

A blockchain-based platform for incentivizing customer reviews in the grocery industry

Nowadays, user-generated content is pivotal for many companies: people trust other customers' opinions more than any brand advertisement. Brands are aware of this and try to promote and motivate their customers to create high-quality content. However, this way of operating is still at an early stage: there is a lack of fairness, as companies typically do not provide a validation system, or if they do, it is not based on a transparent solution, and often there is no reward for creating unique and high-quality content. In this paper, we focus on the problem of incentivizing the user's creation of content in the form of customer reviews in the online grocery industry. Specifically, we illustrate the solution to the problem devised in the Re-Taled project by relying on blockchain technology. We developed a decentralized ecosystem of consumers, influencers, and manufacturers, where content creators are rewarded for their contribution according to a framework that provides incentives in the form of both reputation and monetization. Blockchain technology is used to certify the content's authenticity and compensate content creators with a cryptographic token. We illustrate the technical choices of the solution together with its software architecture and implemented platform. In particular, we introduce the framework used to validate the trustworthiness of user-generated content and favor fairness and transparency within the platform.

Development of a DSP based collaborative computing platform for power system

Abstract With the advancement of information technology, there has been tremendous progress in data production, processing, and storage technology, especially in data processing technology. With the rapid development of the power grid, its operation has become more intelligent and efficient. Traditional power system simulation and analysis tools have disadvantages, such as slow message transmission and the inability to share data, which cannot meet the requirements of modern power system simulation calculations. DSP was introduced and developed by the Southern Power Grid Research Institute and can perform various simulation calculations such as transient stability calculation, power flow calculation, and short circuit calculation. It is widely used in departments such as power planning and design. However, the data calculated by DSP is input through a data card, which is cumbersome and difficult to manage. In order to meet the new requirements of power system simulation analysis, a power system collaborative computing platform has been developed based on DSP, which makes DSP data maintenance and management more efficient and intuitive. It comprehensively improves the efficiency of power grid planning work. This article provides a detailed analysis of the architecture design and application problems of the power system collaborative computing platform, clarifies the requirements of large-scale interconnected power grids for simulation computing data management, and proposes solutions that can meet the needs of simulation computing data management in China’s power grid from two aspects: key data architecture issues and software architecture technology.

High-Performance Spatial Data Analytics: Systematic R&D for Scale-Out and Scale-Up Solutions from the Past to Now

We released open-source software Hadoop-GIS in 2011, and presented and published the work in VLDB 2013. This work initiated the development of a new spatial data analytical ecosystem characterized by its large-scale capacity in both computing and data storage, high scalability, compatibility with low-cost commodity processors in clusters and open-source software. After more than a decade of research and development, this ecosystem has matured and is now serving many applications across various fields. In this paper, we provide the background on why we started this project and give an overview of the original Hadoop-GIS software architecture, along with its unique technical contributions and legacy. We present the evolution of the ecosystem and its current state-of-the-art, which has been influenced by the Hadoop-GIS project. We also describe the ongoing efforts to further enhance this ecosystem with hardware accelerations to meet the increasing demands for low latency and high throughput in various spatial data analysis tasks. Finally, we will summarize the insights gained and lessons learned over more than a decade in pursuing high-performance spatial data analytics.

An architecture for model-based and intelligent automation in DevOps

The increasing complexity of modern systems poses numerous challenges at all stages of system development and operation. Continuous software and system engineering processes, e.g., DevOps, are increasingly adopted and spread across organizations. In parallel, many leading companies have begun to apply artificial intelligence (AI) principles and techniques, including Machine Learning (ML), to improve their products. However, there is no holistic approach that can support and enhance the growing challenges of DevOps. In this paper, we propose a software architecture that provides the foundations of a model-based framework for the development of AI-augmented solutions incorporating methods and tools for continuous software and system engineering and validation. The key characteristic of the proposed architecture is that it allows leveraging the advantages of both AI/ML and Model Driven Engineering (MDE) approaches and techniques in a DevOps context. This architecture has been designed, developed and applied in the context of the European large collaborative project named AIDOaRt. In this paper, we also report on the practical evaluation of this architecture. This evaluation is based on a significant set of technical solutions implemented and applied in the context of different real industrial case studies coming from the AIDOaRt project. Moreover, we analyze the collected results and discuss them according to both architectural and technical challenges we intend to tackle with the proposed architecture.

A theory of degrees in education

Degrees are presumably the most important institution in education markets. However, despite their high relevance, degrees and their design features remain poorly understood within the economics of education. This study addresses this gap. We provide an accurate formal definition of what constitutes a degree and formulate the first theory of degree design. This theory is based on a technological platform view that interprets degrees as modules within a hierarchical structure resembling that of software architectures. This approach provides new opportunities to address questions of degree optimization. Furthermore, a novel strategy to optimize the design of school degrees is proposed. This strategy is expected to improve the learning environment of weaker students across all school types, leaving higher-performing students rather unaffected. The implications and limitations of our approach are also discussed.

Evolving Software Architecture Design in Telemedicine: A PRISMA-based Systematic Review.

This article presents a systematic review of recent advancements in telemedicine architectures for continuous monitoring, providing a comprehensive overview of the evolving software engineering practices underpinning these systems. The review aims to illuminate the critical role of telemedicine in delivering healthcare services, especially during global health crises, and to emphasize the importance of effectiveness, security, interoperability, and scalability in these systems. A systematic review methodology was employed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. As the primary research method, the PubMed, IEEE Xplore, and Scopus databases were searched to identify articles relevant to telemedicine architectures for continuous monitoring. Seventeen articles were selected for analysis, and a methodical approach was employed to investigate and synthesize the findings. The review identified a notable trend towards the integration of emerging technologies into telemedicine architectures. Key areas of focus include interoperability, security, and scalability. Innovations such as cognitive radio technology, behavior-based control architectures, Health Level Seven International (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, cloud computing, decentralized systems, and blockchain technology are addressing challenges in remote healthcare delivery and continuous monitoring. This review highlights major advancements in telemedicine architectures, emphasizing the integration of advanced technologies to improve interoperability, security, and scalability. The findings underscore the successful application of cognitive radio technology, behavior-based control, HL7 FHIR standards, cloud computing, decentralized systems, and blockchain in advancing remote healthcare delivery.

SOFTWARE ARCHITECTURE SYSTEM DESIGN FOR THE MASS SERVICE SYSTEMS MODELING TO BE IMPLEMENTED IN GO PROGRAMMING LANGUAGE

The subject of the article is the methods and approaches to organizing the architecture of software implementation designed for modeling the behavior of mass service systems. The goal of the work is to design a software architecture for implementation in Go language, intended to replicate the behavior of various types of mass service systems, without considering the failure of individual service channels, using parallel computing. The article addresses the following tasks: consider the basis for designing the architecture and conclude its appropriateness; develop requirements for the future software product for more effective resource use and clear definition of successful completion; analyze the approaches to organizing software architecture and make a justified decision on the application of one of them; design a general algorithm scheme taking into account all requirements; identify the components of the modeled system and their interactions; build process diagrams considering the specifics of the Go programming language; define the method and contracts of interaction with the software. The research will utilize the following methods: Go programming language, concurrency, architectural UML diagrams, C4 diagrams, process diagrams. The following results were obtained: the requirements for the software for modeling mass service operations (SMO) were defined; common approaches to organizing architecture were considered and a comparative analysis was conducted; the structure of the future program was developed at the necessary levels of abstraction; for the first time, an architecture of the software product for modeling various mass service systems using parallel computing and the concurrency approach under the implementation in the Go programming language was proposed.

OPTIMIZATION OF THE DEVELOPMENT PROCESS OF MONOLITHIC MULTI-MODULE PROJECTS IN JAVA

In recent years, there has been an increase in the complexity of Java software development and a change in the scope of projects, including an increase in the number of modules in projects. The multi-modularity of projects, although it improves manageability to a certain extent, but often creates a number of problems that can complicate development and, a problem that will appear in the future, require more resources to support. This article will analyze the main problems of monolithic multi-module Java projects and will try to consider a number of possible solutions to overcome the above problems. The article discusses the peculiarities of working with multi-module monolithic projects using Java as the main programming language. The purpose of this article is to identify features and obstacles using the above architectural approach of the software, analysis of the main possible issues of working with the monolithic multi-module Java projects, as well as providing recommendations for eliminating these obstacles or describing the features of the process that could help engineers in supporting this kind of projects. In other word the main goal of this work is to create recommendations, provide modern best practices for working with monolithic multi-modular software architecture and the most popular modern technological solutions used in corporate development. The proposed recommendations allow the team, primarily developers and the engineering side, to avoid possible obstacles that lead to the loss of efficiency of the monolithic software development process. The most important advantage, from the recommendations given in the article, is the optimization of resource costs (time, money and labor) for the development process. As a result of the article, a general list of recommendations was obtained, which allows the developer to better analyze what changes in the project should (if necessary) be made to optimize the development, assembly and deployment processes of a monolithic Java project, as well as advice before designing new software to avoid the main obstacles of monolithic architecture in the future.

Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.

The activation of city squares through sociocultural programming of architecture

The activation of city squares through sociocultural programming of architecture

Deep learning for detection and counting of Nephrops norvegicus from underwater videos

Abstract The Norway lobster (Nephrops norvegicus) is one of the most important fishery items for the EU blue economy. This paper describes a software architecture based on neural networks, designed to identify the presence of N. norvegicus and estimate the number of its individuals per square meter (i.e. stock density) in deep-sea (350–380 m depth) Fishery No-Take Zones of the northwestern Mediterranean. Inferencing models were obtained by training open-source networks with images obtained from frames partitioning of in submarine vehicle videos. Animal detections were also tracked in successive frames of video sequences to avoid biases in individual recounting, offering significant success and precision in detection and density estimations.

Semi-Intrusive Stochastic Galerkin Finite Element Method for Adjoint-Based Optimization Under Uncertainty

The stochastic Galerkin method for the propagation of probabilistically modeled uncertainties can be difficult to apply in practice due to its formulation and the challenge of creating a computational infrastructure to support it. To address these challenges, this work proposes a sampling-based stochastic Galerkin method that leverages existing deterministic analysis and adjoint-based derivative implementations. The proposed formulation is semi-intrusive since it is implemented using an existing deterministic framework, requiring only the numerical sampling of the deterministic residuals, Jacobians, boundary conditions, and adjoint implementations at nodes in the probabilistic domain. The software architectures to support stochastic generalizations of the deterministic finite element frameworks are presented. This proposed approach is demonstrated using a finite element framework for flexible multibody dynamics problems. Finally, the semi-intrusive implementation of the stochastic Galerkin method is used to demonstrate gradient-based optimizations of flexible multibody dynamics systems in the presence of probabilistically modeled uncertainties.

Development of a TGC-based growth model for the olive flounder, Paralichthys olivaceus, and its application in developing a fish growth simulator architecture

Temperature is one of the main factors affecting fish growth. Many studies have proposed a fish growth model considering the effect of temperature. Among these models, the TGC (thermal growth coefficient) model which digitized the influence of temperature on fish growth is the most notable. The original TGC model was made in the form of applying 1/3 as an exponent, but subsequent studies have shown that it is necessary to apply different exponent value or other constant depending on the dynamics of growth. In this study, the original TGC model using 1/3 as an exponent and the new model using 2/3 as an exponent were compared for olive flounder (Paralichthys olivaceus). The seasonal temperature function under the conditions of the flow-through system was applied and the transition point of change in the growth dynamics was obtained by comparing the instantaneous growth rate of the two TGC models. Around 541 g of the transition point was obtained, and a combined TGC model was presented that integrated the two models. However, the growth prediction model based on these statistical techniques does not reflect real-time changes in each parameter and requires academic knowledge, making it difficult to use in the actual field. Recently, as the smart aquaculture industry incorporating ICT (information and communication technologies) has grown rapidly, many solutions such as fish growth prediction simulators using statistical growth models have been developed. Therefore, in this study, input and output variables were classified and software architectures were presented so that the statistical form of fish growth model using TGC derived above could be applied when developing a fish growth prediction simulator. Deriving these growth models and interpreting them into languages in the field of ICT will enhance the field applicability of academic research results as a part of smart aquaculture technology.

Using Version Control Information to Visualize Developers' Knowledge

It is not always clear in case of a software project who has the right amount of knowledge concerning a certain module or file. Programmers frequently ask questions like "Who knows the most about this code?" or "Who can I ask for help when I work on this module?". In a large, long-term software product, knowledge is distributed in an uneven way among developers. Developer fluctuation during the product lifetime might cause some parts of the code to be known very well by a multitude of developers, while other parts might sink to the "gray zone", where developer competence is dangerously scarce. It is important for the project management to identify such critical points, to avoid the complete loss of competence. Version control repositories contain loads of useful information about the evolution of a software project. This paper presents a developer-centered visualization that is intended to show individual, team-bound and company-bound knowledge of large legacy projects. The competence information is computed from the extracted version control information from Git repositories. The calculated competence value is based on the number of commits per developer and their significance. Aggregated views for teams and companies are available based on various heuristics. The visualization is implemented as a new plugin in CodeCompass, a standalone, open-source code comprehension software. Project managers and individual developers may both profit from the tool, whether it concerns software evolution, human-resource management, architecture, knowledge catch-up, or blame.

A Framework and Taxonomy for Characterizing the Applicability of Software Architecture Recovery Approaches: A Tertiary‐Mapping Study

SummarySoftware architecture assists developers in addressing non‐functional requirements and in maintaining, debugging, and upgrading their software systems. Consequently, consistency between the designed architecture and the implemented software system itself is important; without this consistency the non‐functional requirements targeted may not be addressed and architectural documentation may mis‐direct maintenance efforts that target the associated code‐base. But often, when software is initially implemented or subsequently evolved, the designed architecture and software architecture become inconsistent, with the implemented structure degraded due to issues like developer time‐pressures, or ambiguous communication of the designed architecture. In such cases, Software Architecture Recovery (SAR) or consistency approaches can be applied to reconstruct the architecture of the software system and possibly to compare it to/re‐align it with the designed architecture. Many SAR approaches have been proposed in the research. However, choosing an appropriate architecture recovery approach for software systems is still an open issue. Consequently, this research aims to conduct a tertiary‐mapping study based on available secondary studies of architecture recovery approaches, to uncover important characteristics, towards the selection of appropriate SAR approaches. This research has aggregated 13 secondary studies and 10 primary studies beyond 2020 from 5 databases and, in doing so, identified 111 architecture recovery approaches. Based on these approaches, a taxonomy, containing nine main SAR‐selection categories is proposed and a framework (in the form of a supporting tool to help developers select an appropriate SAR approach) has been developed. Finally, this research identifies six potential open research gaps related to the underlying research that could be helpful for guiding research in the future.

Task-Motion Planning System for Socially Viable Service Robots Based on Object Manipulation.

This paper presents a software architecture to implement a task-motion planning system that can improve human-robot interactions by including social behavior when social robots provide services related to object manipulation to users. The proposed system incorporates four main modules: knowledge reasoning, perception, task planning, and motion planning for autonomous service. This system adds constraints to the robot motions based on the recognition of the object affordance from the perception module and environment states from the knowledge reasoning module. Thus, the system performs task planning by adjusting the goal of the task to be performed, and motion planning based on the functional aspects of the object, enabling the robot to execute actions consistent with social behavior to respond to the user's intent and the task environment. The system is verified through simulated experiments consisting of several object manipulation services such as handover and delivery. The results show that, by using the proposed system, the robot can provide different services depending on the situation, even if it performs the same tasks. In addition, the system demonstrates a modular structure that enables the expansion of the available services by defining additional actions and diverse planning modules.

Developing a Performance Evaluation Benchmark for Event Sourcing Databases

In the domain of software architecture, Event Sourcing (ES) has emerged as a significant paradigm, especially for systems requiring high levels of auditability, traceability, and intricate state management. Systems such as financial transaction platforms, inventory management systems, customer relationship management (CRM) software, and any application requiring a detailed audit trail can significantly benefit from this approach. Numerous aspects of ES remain unexplored, as they have yet to be thoroughly investigated by scientific research. The unique demands of such systems, particularly in terms of database performance and functionality, are not adequately addressed by existing database benchmarks. By establishing benchmarks, organizations can compare different databases to determine which best meets their needs for applications. This aids in selecting the most appropriate technology based on empirical data rather than assumptions or marketing claims.This paper introduces a novel benchmarking framework specifically designed for evaluating databases in the context of event sourcing. The framework addresses critical aspects unique to ES, including event append performance, efficient handling of Projections (separate databases for read operations), strong consistency, ordered data insertion, and robust versioning controls. Through rigorous testing and analysis, this framework aims to fill the gap in existing database benchmarking tools, providing a more accurate and relevant assessment for ES systems. We also conducted experiments that not only demonstrated the effectiveness of our approach but also yielded meaningful results, substantiating its practicality and applicability.

Distributed Transactions in Microservice Architecture: Informed Decision-making Strategies

The emergence of microservice architecture has revolutionized software development practices by decentralizing components, facilitating scalability, and enabling agility in system design and deployment. There are some benefits of incorporating microservices instead of a single server, however, distributed components introduce extra constraints and complexities in maintaining data consistency as well. As microservices interact independently, coordinating data updates across multiple services becomes challenging, particularly in scenarios where transactional integrity is required. Distributed transactions are one of the solutions for ensuring data consistency across services. Regardless of effectiveness distributed transactions entail different trade-offs and performance implications. Those trade-offs are not always justified. This study highlights the need for a nuanced understanding of distributed transactions in microservices by revisiting challenges in managing distributed transactions within data storage systems. It also represents existing solutions to the different distributed transaction methods. In this paper, through experiments comparing microservices and monolithic systems, the impact of distributed transactions on system performance is evaluated, giving intuition about consequences when a single data source transaction is migrated to the distributed environment. This research also contributes to enhancing understanding and decision-making regarding the utilization of distributed transactions in a microservices architecture. Ultimately, this paper presents an optimized decision-framework for the application of distributed transactions in microservices architecture, aiming to simplify and expedite processes of software architecture for software engineers, solution architects, and developers.