Abstract : The article examines modern software testing practices in e-commerce and their influence on the performance and economic efficiency of digital platforms. It analyzes the role of quality assurance integration into CI/CD workflows, the automation of user scenarios, and real-time monitoring. The study highlights that the maturity of the testing infrastructure directly affects key metrics such as time-to-market, system resilience under load, and ROI. The implementation of testing strategies under conditions of growing digital consumption contributes to incident reduction, conversion rate improvement, and customer retention. The article concludes that quality assurance should be viewed as a strategic asset of a digital platform.

Abstract : This study examines how modern performance optimization techniques influence the efficiency of frontend applications. Key techniques such as code splitting, lazy loading, tree shaking, resources minimization and compression, use of modern bundlers (Webpack, Vite), and use of Content Delivery Networks (CDN) are examined. The influence of these methods on key performance metrics is analyzed. Additionally, research and real-world case studies are considered to illustrate the effects of both optimization strategies and loading delays on user behaviour, engagement levels, and business indicators.

Abstract : This paper explores the choice between synchronous and asynchronous interaction models in microservice architecture, focusing on their impact on system performance and scalability. Key characteristics such as latency, throughput, and scalability under varying loads are discussed. The interaction models based on REST API and event-driven approaches are compared, emphasizing their advantages and disadvantages in the context of high-load distributed systems. The analysis presented aids in selecting the optimal interaction model based on system-specific requirements, such as data consistency, fault tolerance, and processing efficiency.

Abstract : This article examines modern dynamic traffic control mechanisms in distributed computing systems as a means of enhancing the adaptability and fault tolerance of digital infrastructure. It analyzes load balancing algorithms, adaptive rate limiting, low-priority request shedding, and resource scaling strategies. Special attention is given to the integration of these algorithms into cloud-native architectures. The article emphasizes the role of observability as a foundational element enabling the transition from reactive to proactive traffic management. It concludes that the combined application of these mechanisms establishes a robust architectural foundation for the stable operation of distributed systems under high-load conditions.

Abstract : This article examines the integration of DevOps practices into the development and operations processes of applications built with the Ruby programming language. Particular attention is given to the architectural and organizational characteristics of Ruby projects that influence the implementation of automated delivery, testing, and maintenance workflows. It explores the use of modern DevOps tools such as Docker, Kubernetes, and Terraform within the Ruby ecosystem, focusing on their role in ensuring environment reproducibility, service orchestration, and infrastructure as code management. The impact of these solutions on improving the resilience, scalability, and reliability of the application lifecycle is investigated, along with practical automation cases and typical implementation schemes.

Abstract : The article takes into account architectural patterns used when building high-load distributed systems, such as CQRS, Event Sourcing, and Circuit Breaker. It analyzes their rules of operation, typical use cases, and shortcomings in terms of scalability, consistency, and fault tolerance. The paper presents a functional classification of these patterns, highlighting their engineering advantages and operational risks. Real-world implementation examples from major technology companies are provided, and criteria for selecting architectural solutions are formulated based on workload characteristics and infrastructure maturity. The article concludes by emphasizing the rationale for combining patterns within a unified architectural strategy, taking into account the specifics of business logic and system availability requirements.

Abstract : This article examines the digital visualization of investment activity in the EOS (Vaulta) blockchain ecosystem. The architecture of the platform based on the Delegated Proof of Stake mechanism is analyzed, as well as the specifics of the investment environment shaped by the resource model and the structure of incentives. Directions of financial activity and their impact on the behavior of ecosystem participants are studied. Special attention is paid to the structure of blockchain data and its analytical potential, as well as to the role of interface and visual-analytical tools in the interpretation of investment processes. User experience and interface are analyzed, ensuring cognitive accessibility of information, reduction of user workload, and increased trust in decentralized services.

Abstract : This article examines the role of predictive analytics, based on machine learning methods, in enhancing the efficiency of operations management. It explores approaches to the application of predictive analytics in cost management, resource and supply chain management, as well as in maintenance planning. Particular attention is given to the resilience of business processes and the reduction of inefficiencies through accurate forecasting and the integration of analytical tools into strategic planning. It investigates the potential of predictive models to reduce costs, improve equipment reliability, and optimize supply chains.

Abstract : This paper examines feature selection methods in machine learning tasks and their impact on model performance and interpretability. The problem of high-dimensional data remains one of the most critical challenges in modern analytics, as redundant and irrelevant features increase the risk of overfitting, complicate computations, and reduce the transparency of conclusions. The aim of this study is to provide a comparative analysis of filter, wrapper, and embedded feature selection methods, as well as interpretation techniques based on SHAP, with an emphasis on their practical application. As an example, the widely used California Housing dataset was employed for modeling and feature importance evaluation. The analysis utilized permutation importance, partial dependence plots, and SHAP to assess and compare the relevance of features.

Abstract : This article explores the evolution of web crawling systems used in modern search platforms. It examines key stages in the development of crawler architectures, ranging from simple sequential traversal to large-scale distributed systems. It analyzes various crawl scheduling methods, including URL loading strategies, queuing mechanisms, content change prediction, and adherence to web resource access constraints. Particular attention is given to URL prioritization as a critical factor influencing index completeness, the timely inclusion of new pages, and the overall effectiveness of search results. It also discusses machine learning–based approaches used to predict the importance of web pages and to improve the efficiency of crawler resource allocation.