Iryna Pikh

Prototyping is an important component of user interface development, during which key decisions are made regarding methods of information presentation and the organization of user interaction. Assessing inclusivity at this stage makes it possible to account for relevant factors before the final implementation of the interface and to adjust design decisions accordingly. The goal of this study is to develop a model for fuzzy multi-criteria assessment of user interface inclusivity at the prototyping stage, taking into account the relative importance of the criteria and the preference degree of alternatives. The relative priorities of the criteria are established by the pairwise comparison method using the Saaty scale. To demonstrate the application of the model, three alternative interface prototypes are considered. For their comparison, fuzzy preference relations are formed for each criterion, and their intersections and weighted convolutions are performed. Sets of non-dominated alternatives and the resulting membership function are determined. A structural–functional model and pseudocode of the assessment algorithm are developed. The proposed approach makes it possible to quantitatively compare prototypes based on a set of inclusivity factors and determine the optimal interface solution.

The paper substantiates the factors that give rise to crisis situations and conflicts during the management of energy-active facilities – situations that frequently escalate into emergencies requiring rapid response and effective decision-making. In such contexts, it is crucial to examine the psychological and intellectual resilience of operators, as this resilience underpins the capacity of systems and agents to function within the scope of defined objectives and authorities. Such resilience serves as a key factor in maintaining functional efficiency when countering various types of threats and attacks. A significant aspect of functional resilience involves analysing the logic behind decision-making aimed at achieving system-oriented outcomes. These decisions are grounded in principles of coherence, multi-level analysis, and risk assessment. Identifying the structure of this management logic and the associated decision-making procedures enables the development of efficient algorithms for addressing crisis situations – algorithms applicable across diverse fields, ranging from security management to socio-economic systems. The challenge of making efficient decisions – whether by an intelligent agent (a team player), an operational team, or a managerial/administrative structure – is determined by a complex interplay of psycho-neurophysiological and intellectual factors, operating at both the individual and team levels. They are participants in a game structure that shapes the strategy of permissible actions. To address this problem, the paper develops a cognitive-logical concept for the operational resilience of cyber-physical infrastructure, which integrates a “human-agent” into a unified situational control loop. Based on the category algebra, a category-theoretic information model is constructed for the decomposition of a hierarchical production structure, accounting for 16 threat-resilient personnel levels (P1–P16). By integrating H. Gardner’s theory of multiple intelligences and R. Sternberg’s triarchic theory, a cognitive map for operator decision-making during laser-based environmental monitoring is formalized. A mathematical framework for the quantitative assessment of personnel actions is developed, incorporating cognitive coefficients (KF), confidence levels (K(B)), and error risk factors (Ri). Practical testing of the system at the Burshtyn Power Station (Ukraine) and glass-manufacturing companies has demonstrated its efficiency under extreme operating conditions (1200°C–1500°C). Implementation of this method has reduced the reaction time of operational and dispatch personnel during pre-emergency and emergency conditions (Alarm/Avaria) and minimized the overall risk of error decisions stemming from the human factor.

Prototyping is an important component of user interface development, during which key decisions are made regarding methods of information presentation and the organization of user interaction. Assessing inclusivity at this stage makes it possible to account for relevant factors before the final implementation of the interface and to adjust design decisions accordingly. The goal of the study is to develop a model for fuzzy multi-criteria assessment of the user interface inclusivity at the prototyping stage, taking into account the relative importance of factors and the preference degree of alternatives. The relative priority of factors is established by the pairwise comparison method using the Saaty scale. To demonstrate the application of the model, three different options for the interface solution at the prototyping stage are considered. For their comparison, fuzzy preference relations are formed for each factor, their intersection and weighted convolution are performed. Sets of non-dominated alternatives and the resulting membership function are determined. A structural-functional model and pseudocode of the assessment algorithm are developed. The proposed approach makes it possible to quantitatively compare prototypes based on a set of inclusivity factors and determine the optimal interface solution.

The quality of vector images depends on a significant set of geometric and structural factors, which makes objective assessment a challenging task. This paper proposes a comprehensive approach to identifying and prioritizing these factors. Factor selection was performed based on expert evaluation and analysis of inter-factor relationships. A reachability matrix of factors was constructed to analyze direct and indirect relationships. Models describing relationships between the factors were developed. The rank and weight of each factor were calculated using a dependency-weighting system. An information system was developed to automate the process of prioritizing factors based on the proposed methodology. The software architecture was implemented in Python 3.13.5 using the Tkinter, NumPy, and NetworkX libraries. Experimental results confirmed that the factor «coordinate accuracy» has the highest level of significance, whereas «file format» has the smallest influence on the quality of vector images. Due to the lack of dependence on specific selected factors, the developed system is universal and suitable for prioritizing factors in any application domain. Future research will focus on integrating the developed information system into a fuzzy-logic-based system for assessing the quality of vector images.

The quality of vector images depends on a significant set of geometric and structural factors, which makes objective assessment a challenging task. This paper proposes a comprehensive approach to identifying and prioritizing these factors. Recursive feature elimination based on a random forest model was applied. A reachability matrix of factors was constructed to analyze direct and indirect relationships. Models describing relationships between the factors were developed. The rank and weight of each factor were calculated using a dependency-weighting system. An information system was developed to automate the process of prioritizing factors based on the proposed methodology. The software architecture was implemented in Python using the Tkinter, NumPy, and NetworkX libraries. Experimental results confirmed that the factor «coordinate accuracy» has the highest level of significance, whereas «file format» has the smallest influence on the quality of vector images. Due to the lack of dependence on specific selected factors, the developed system is universal and suitable for prioritizing factors in any application domain. Future research will focus on integrating the developed information system into a fuzzy-logic-based system for assessing the quality of vector images.

Duplicate bug reports increase the workload of software engineering teams and delay the resolution of critical issues, making automated detection essential. This paper presents a two-stage approach that combines transformer-based semantic retrieval with classical machine-learning classification. First, text features of the defect are vectorised using transformer models such as BERT (Bidirectional Encoder Representations from Transformers, google-bert/bert-base-uncased), MiniLM (Miniature Language Model, sentence-transformers/all-MiniLM-L6-v2) or MPNet (Masked and Permuted Pre-training for Language Understanding, sentence-transformers/all-mpnet-base-v2) to identify semantically similar reports and narrow the candidate search space. Second, the filtered pairs are classified using algorithms such as XGBoost (eXtreme Gradient Boosting), SVM (Support Vector Machines) or logistic regression to determine true duplicates. This hybrid method improves accuracy while substantially lowering computational cost. Experimental results validate the proposed approach, demonstrating robust accuracy and consistent performance in identifying duplicate defect reports.

The presence of duplicate bugs in defect tracking systems creates an additional burden on software engineering specialists, potentially causing delays in fixing critical bugs. The use of automated methods for detecting duplicates relieves this burden and re-duces the time and cost associated with their processing. Detecting duplicate bug re-ports in large databases is a challenging task that requires a balance between compu-tational efficiency and prediction accuracy. Traditional approaches either rely on re-source-intensive searches or use classification models that, while highly accurate, compromise performance. This paper proposes a new approach to automatic duplicate bug detection based on a two-level analysis of text features in reports. The first stage involves vectorising text data using BERT (Bidirectional Encoder Representations from Transformers), MiniLM (Miniature Language Model) and MPNet (Masked and Per-muted Pre-training for Language Understanding) transformer models, which deter-mine the semantic similarity between defect descriptions. This reduces the number of potential duplicates and the volume of reports that need to be compared. The second stage involves classifying pairs of potential duplicates using machine learning algo-rithms, including XGBoost (eXtreme Gradient Boosting), SVM (Support Vector Ma-chines) and logistic regression. The models are trained on vector representations of text to assess the degree of similarity between errors. The combination of transformer mod-els with classical classification algorithms ensures high accuracy in detecting dupli-cates while significantly reducing query processing time. The results of the experi-ments confirm the effectiveness of the approach, demonstrating its ability to reduce the number of required comparisons, cut the cost of analysing defect reports, and achieve sufficient accuracy in duplicate detection.

The article examines Key Performance Indicators (KPIs) as a tool for assessing the quality and performance of web applications. A classification of KPIs is proposed based on technical, user-oriented, and business-related criteria. The paper analyzes current approaches to measuring each of these categories and provides examples of KPI implementation in various types of web systems. Special attention is given to the relationship between technical metrics and the quality of user perception. The findings may be useful for software engineers, analysts, and project managers in planning the development and optimization of web applications.

The development of web applications is often carried out within tight deadlines and under limited resources, which complicates adherence to the principles of high-quality design. The design of modern web applications requires consideration of a wide range of criteria that affect usability, accessibility, and the overall effectiveness of user interaction. Given the increasing complexity of digital interfaces, there is a growing need for the development of a theoretically grounded approach to evaluating design quality. The aim of this study is to develop semantic models for formalizing the factors influencing the quality of web application design. The application of formalized models makes it possible to systematize expert knowledge, enhance the objectivity of decision-making, and ensure the reproducibility of results regardless of subjective factors. This article identifies a set of factors that influence the quality of web application design. To achieve this, methods of expert evaluation and semantic modeling are employed. For the quantitative analysis of criterion significance, the rating scale method is applied, and indicators of variation and consistency of expert judgments are calculated. Three main categories of criteria are defined: ergonomics and cognitive principles of interaction; accessibility and inclusivity; and information architecture and visual design. Semantic models representing the relationships between factors within each category are constructed. The proposed approach enables the formalization of expert knowledge and provides a foundation for the further automated evaluation of web application design quality using fuzzy logic methods. The developed semantic models can also be integrated into decision support systems for the design of digital products. Furthermore, they may serve as a basis for the development of educational materials and tools for auditing web application design in accordance with contemporary standards.