Abstract: This study quantitatively evaluates the performance of a non-invasive hybrid brain–computer interface (BCI) compared to a conventional mouse in pointing (point-and-click) tasks. A commercial wearable BCI (Brainfingers), based on electromyography (EMG) and electrooculography (EOG) signals with low-level electroencephalography (EEG) components, was assessed against a Microsoft Optical Mouse using ISO/TS 9241-411-based one-dimensional (1D) and two-dimensional (2D) target acquisition tasks. Pointer coordinates were recorded and analyzed using Fitts’ law metrics. A total of 48 non-disabled participants completed the experiments. The results reveal significant performance differences between the two input devices. The BCI device exhibits substantially lower performance than the mouse across the reported Fitts’ law measures. Mean throughput was 0.35 bits/s for the BCI and 6.03 bits/s for the mouse in the 1D tests, and 0.43 bits/s for the BCI and 5.17 bits/s for the mouse in the 2D tests. Despite the BCI’s low performance and although the present experiments involved non-disabled participants, the findings, considered alongside prior literature on Brainfingers and non-invasive BCIs for computer access, suggest that the device may still have assistive technology value for users with severe motor impairments.

Abstract: Except for coding, Large Language Models (LLMs) are increasingly explored as assistants for software design and architectural tasks. However, it remains unclear to what extent LLMs can reliably apply explicitly requested software architectural patterns when provided with user-defined specific requirements. In this paper, we empirically evaluate the ability of multiple LLMs to instantiate specific architectural styles under controlled conditions. We conduct a series of experiments in which models are prompted with problem descriptions expressed at different levels of structure, ranging from free-structured requirement lists to complete Software Requirements Specification (SRS) documents. The models are instructed, using single-shot prompts, to generate architectures in four representative styles: client–server, 3-tier, Model–View–Controller (MVC), and microservices. The authors assess the generated architectures with respect to structural correctness, requirement coverage, and adherence to the requested architectural pattern. Our results show that while LLMs can correctly apply simpler architectural patterns, performance decreases as architectural complexity and problem size increase. Model size and requirement representation significantly influence pattern adherence, whereas Retrieval Augmented Generation (RAG) exhibits mixed effects depending mainly on the material context and the LLMs capacity. These findings provide insight into the current capabilities and limitations of LLMs in architectural pattern application and inform the design of future AI-assisted architectural tools.

Abstract: Microservice-based architecture has become a dominant style for building cloud-native systems, but their rapid and continuous evolution makes it difficult to understand, verify, and control the impact of change. Although many works address microservices from the perspectives of design, deployment, and operation, there is still limited consolidated knowledge on how the evolution and verification of microservice-based systems are supported in the research literature. This paper reports on a systematic literature review (SLR) on methods and tools for analyzing evolution and providing verification support in microservice-based systems. Following established SLR guidelines, we defined a review protocol with focused research questions, executed structured searches in major digital libraries, and applied explicit inclusion and exclusion criteria tailored to cloud-native microservices. Candidate studies were screened through multiple stages, supported by an academic queries table, a statistics table summarizing the selection process, and a quality assessment checklist. For each included primary study, we extracted data on the type of evolution or change considered, the analysis or verification technique used, the form of tool support, and the nature of empirical evaluation. The review highlights a small but emerging body of work that combines microservice evolution with formal verification, runtime monitoring, and model-based analysis. However, empirical evidence is often limited to small case studies, with few industrial-scale evaluations and little shared tooling or datasets. We conclude by identifying gaps and outlining a research agenda for more robust, empirically grounded approaches to microservice evolution and verification.

Abstract: Code smells are indicators of potential design problems in software systems and are commonly used to guide refactoring activities. Recent advances in representation learning have enabled the use of embedding-based models for analyzing source code, offering an alternative to traditional approaches based on manually engineered metrics. However, the effectiveness of different embedding representations for multiclass code smell detection remains insufficiently explored. This study presents an empirical comparison of embedding models for the automatic detection of three widely studied code smells: Long Method, God Class, and Feature Envy. Using the Crowdsmelling dataset as an empirical basis, source code fragments were extracted from the original projects and transformed into vector representations using two embedding approaches: a general-purpose embedding model and the code-specialized CodeBERT model. The resulting representations were evaluated using several machine learning classifiers under a stratified group-based validation protocol. The results show that CodeBERT consistently outperforms the general-purpose embeddings across multiple evaluation metrics, including balanced accuracy, macro F1-score, and Matthews correlation coefficient. Dimensionality reduction analyses using PCA and t-SNE further indicate that CodeBERT organizes code smell instances in a more structured latent representation space, which facilitates the separation of smell categories. These findings provide empirical evidence that domain-specific pretraining plays an important role in representation learning for software engineering tasks and that embedding choice significantly influences the separability of code smell categories in multiclass detection settings.

Abstract: This research work deals with the challenges in software fault prediction (SFP) such as class imbalance in benchmark datasets, noisy features, and high-dimensional feature spaces. To overcome the above limitations, we propose a novel hybrid feature selection framework, FS-BWOA–COA, which incorporates Coati Optimization Algorithm (COA) for local exploitation and Beluga Whale Optimization Algorithm (BWOA) for global exploration. The two-phase optimization approach helps to avoid duplication and improves the stability of the classifier and also helps in maintaining the balance between exploration and exploitation. The framework was tested using several classifiers such as Decision Tree, SVM, KNN, and Naïve Bayes on eleven NASA PROMISE datasets. The hybrid outperforms single BWOA and COA, with an average accuracy of 0.9033 and peak values of 0.95 on the MC1 and JM1 datasets. The results of the statistical validation using the Friedman test, Wilcoxon signed-rank test, and paired t-tests confirm the same.

Abstract: Recent advances in large language models (LLMs) have made it feasible to use them as automated debugging tutors, but it remains unclear how much can be gained by moving from single-model tutors to multi-agent councils with separated roles. We study this question in an offline simulation on 200 debugging cases drawn from an online judge, spanning 20 problems split into course-style and contest-style challenge tracks. We compare four single-model tutors based on current frontier models with four councils that assign models to Architect, Skeptic, Secretary, Pedagogue, and Mentor roles and operate in both Blind and Guided modes. Single-model tutors achieve near-perfect repair on course problems but perform less reliably on challenge cases and often rewrite large portions of student code, show non-negligible false positive rates, and leak full or near-full solutions in a substantial share of hints. Councils designed around measured model strengths improve both technical and pedagogical behaviour. On the challenge track, the best council raises patch success by 12.2 percentage points over the best single tutor, while reducing false positives, shrinking median patch size, improving hint localisation, and cutting solution leakage in Blind mode from about one fifth of hints to under ten percent. Councils also exhibit higher stability across reruns and produce hints that two independent instructors consistently rate as more useful and better scaffolded. Guided mode, where internal components see a reference solution, yields further technical gains but introduces leakage risks that require prompt tightening and a sanitising Secretary to control the flow of ground truth. Additional trap experiments with poisoned reference solutions show a mix of resistance and fail-safe collapse rather than systematic poisoning of hints. These results indicate that orchestration and information flow are powerful levers and that well-designed councils can provide more reliable and pedagogically aligned debugging support than strong single-model tutors alone.

Abstract: A common recommendation for preventing brute-force authentication, credential stuffing, scraping, and resource exhaustion is rate limiting, a standard control for API availability and security. Uncertain semantics under horizontal scaling, proxy-induced client-IP ambiguity, unsafe identity binding, ambiguous policy resolution, canonicalization gaps in endpoint matching, and unbounded in-memory state are some of the reasons why deployments frequently fail. In this paper, we propose an endpoint-aware mechanism that uses token buckets with bounded bucket state to enforce per-endpoint RPS limits that are configured in a database and cached for low-latency lookup. The mechanism offers robust endpoint normalization (including an explicit UNKNOWN bucket for unmatched paths), deterministic policy precedence, strict identity validation, trusted-proxy boundaries for client IP extraction, optional per-endpoint cost weights, and operational controls for safe defaults and cache reload. We propose adversarial test suites and microbenchmarks for reproducible evaluation and structure a security analysis by attack surfaces (identity, endpoint normalization, state management, configuration, and distributed enforcement). Additionally, we compare the mechanism to commonly used gateway/edge systems and shared-state primitives, and place it within related work on distributed algorithms, adaptive controllers, overload control, and API rate-limit adoption patterns.

Abstract: Repeated measures ANOVA is the statistical method for comparing means of the same sample measured at least two different times, or two different contexts. It may also be used to compare means between two or more related groups. This paper serves as a tutorial for repeated measures ANOVA using R. It will introduce readers to parametric, nonparametric and robust one-way repeated measures ANOVA using the rstatix, afex, WRS2, and ARTool packages.

Abstract: The philanthropic sector faces persistent challenges in transparency and accountability that fundamentally undermine donor trust. While blockchain technology offers an immutable transaction ledger, it cannot inherently verify whether donated funds achieve their intended real-world impact. This paper presents a novel decentralized application that addresses this critical gap through the integration of Ethereum Smart Contracts with an AI-driven auditing pipeline. The system introduces a milestone-based fund release mechanism requiring NGOs to submit vendor invoices as documentary proof of expenditure. These documents undergo Optical Character Recognition followed by credibility assessment using Google Gemini, a Large Language Model that performs semantic analysis of financial documents. A two-thirds multi-signature consensus by platform administrators authorizes direct on-chain payments to verified ven dors. The primary innovation lies in bridging the oracle problem through automated AI verification of off chain documentary evidence controlling on-chain fund disbursement. Experimental validation demonstrates that this hybrid approach effectively automates credibility verification while ensuring every released fund is backed by verifiable evidence, achieving significant performance improvements in fraud detection and accountability enforcement compared to purely manual or purely blockchain-based systems.

Abstract: Software engineering activities have shifted from building new systems to maintaining 2 code bases. Yet teaching software engineering rarely echoes that reality. It is easier to teach 3 students to develop software from scratch rather than guide them to maintain an existing 4 project by suggesting and implementing modifications and enhancements. We believe that 5 the best way to teach maintenance is through practical experience. This paper describes the 6 evolution of an agent modelling tool called AMMBER. It started as a software engineering 7 team project eight years ago. Tens of students have been involved in the subsequent years 8 in maintaining and extending the software through projects in capstone units, internships 9 and casual employment. The paper authors are the current team maintaining the software. 10 The maintenance activities have included corrective maintenance, adaptive maintenance 11 and perfective maintenance, and we describe lessons learned through our failures and 12 successes. We advocate exposing students to maintenance activities on active code bases 13 and share lessons learned about increasing the capability of software engineering students 14 to usefully perform maintenance activities.

Abstract: The augmented assembly language @Asm is proposed in order to transcend the fragmentation of architecture-specific dialects, to provide a unified framework for diverse processing paradigms as a universal assembly language and to function as a self-compiling bootstrap instrument adaptable to any processor system. The language augmentations include: flexible machine-language descriptions, general memory and data management directives, custom lexical identification through regular expressions, parsing facilities, generalized macroprocessing, flexible assembly control instructions, customizable encoding and code generation features, compiler-oriented abstraction mechanisms at the language level. The native abstraction augmentations enable expressive and concise high-level descriptions within assembly language for any present, emerging, or future systems.

Abstract: This paper presents a compositional Artificial Intelligence (AI) service pipeline for generating interactive structured data from raw scanned images. Unlike conventional document digitization approaches, which primarily emphasize optical character recognition (OCR) or static metadata extraction, the proposed framework adopts a modular architecture that decomposes the problem into specialized AI services and orchestrates them to achieve higher-level functionality. The pipeline integrates core services including OCR for text conversion, image recognition for embedded visual content, interactive form modelling for structured data and NLP for extraction of structured representations from raw text. The form models incorporate various rules like value-type filtering and domain-aware constraints, thereby enabling normalization and disambiguation across heterogeneous document sources. A key contribution is the interactive browser linking extracted structures back to the original scanned images, thus facilitating bidirectional navigation between unstructured input and structured content. This functionality enhances interpretability, supports error analysis, and preserves the provenance of extracted information. Furthermore, the compositional design allows each service to be independently optimized, replaced, or extended, ensuring scalability and adaptability to diverse application domains such as press archives, enterprise repositories and government documents.

Abstract: Architecture viewpoints play a central role as an abstraction mechanism for structuring, communicating, and reviewing software architectures by separating concerns and addressing diverse stakeholder needs [1] [8] [9] [11]. However, in both industrial practice and academic research, viewpoint definitions are often fragmented, inconsistently expressed, or narrowly scoped, which limits comparability, reuse, and long-term architectural evolution [10] [14] [15]. Existing architecture frameworks and standards, including TOGAF, C4, and ISO/IEC/IEEE 42010, either emphasize processes and notations or deliberately avoid prescribing concrete viewpoint sets [1] [4] [33]. While this flexibility supports broad applicability, it also leaves practitioners without a reusable reference taxonomy that systematically consolidates architectural concerns encountered in modern software-intensive systems [8] [10] [11]. This paper introduces PACT (Practical Architecture Viewpoint Taxonomy), a reference-level taxonomy of architecture viewpoints that consolidates recurring architectural concerns observed across standards, established viewpoint models, and industrial practice. PACT defines 52 viewpoints spanning business, application, integration, data, security, infrastructure, governance, and operations concerns. Each viewpoint is specified using a unified definition template that captures its primary concern, key questions, stakeholders, abstraction focus, and scope, enabling systematic comparison, selection, and reuse [1] [11]. PACT is explicitly aligned with the conceptual model of ISO/IEC/IEEE 42010, while remaining method-, notation-, and tool-independent, enabling reuse across heterogeneous architectural practices without imposing process or documentation lock-in [1]. It is intended as a reference taxonomy rather than a prescriptive framework, supporting enterprise architecture governance and system design practices [6] [7], as well as academic analysis of architectural knowledge and viewpoints [15] [18]. A case-based evaluation and industrial illustrations illustrate how PACT supports more systematic concern coverage, improved clarity, and structured architecture reviews across heterogeneous systems. The taxonomy is designed to be extensible, providing a stable reference for future research and evolving architectural practices [10] [32].

Abstract: Wireless Sensor Networks (WSNs) have diverse applications in urban, industrial and environmental monitoring. However, the design complexity of this type of network is high, due to conflicting objectives such as latency, energy consumption, connectivity and coverage. This article addresses the need for structured and reproducible approaches to developing WSNs. We propose a modular and scalable system designed to integrate simulators and evolutionary algorithms for multi-objective optimization in WSNs. We present a formalized process and supporting architecture that combines containerized simulations, a reactive data management layer, and a flexible optimization engine capable of handling diverse objective formulations and search strategies. The proposed environment enables distributed, simulation-based optimization experiments with automated orchestration, persistent metadata and versioned execution artifacts. To demonstrate feasibility, we present a prototype implementation that incorporates synthetic test modules and real WSN simulations using a classical simulator for simulating sensor networks. The results illustrate the potential of the proposed system to support reproducible and extensible research in design and optimization of WSNs.

Abstract: Mobile Software Engineering has emerged as a distinct subfield, raising questions about the transferability of its research findings to general Software Engineering. This paper addresses the challenge of evaluating the generalizability of mobile-specific research, using Green Computing as a representative case. We propose a systematic method that combines a mapping study to identify potentially overlooked mobile-specific papers with a focused literature review to assess their broader relevance. Applying this approach, we find that several mobile-specific studies offer insights applicable beyond their original context, particularly in areas such as energy efficiency guidelines, measurement, and trade-offs. The results demonstrate that systematic identification and evaluation can reveal valuable contributions for the wider Software Engineering community. The proposed method provides a structured framework for future research to assess the generalizability of findings from specialized domains, fostering greater integration and knowledge transfer across Software Engineering disciplines.

Abstract: This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges for traditional EDA methodologies. While EDA tools excel at design and simulation, they often operate as siloed applications, lacking the semantic context necessary for intelligent fault diagnosis and system-level optimization. Sem4EDA addresses this gap by providing a comprehensive ontological framework developed in OWL 2, creating a unified, machine-interpretable model of hardware components, EDA design processes, fault modalities, and IoT operational contexts. We present a rule-based reasoning system implemented through SPARQL queries, which operates atop this knowledge base to automate the detection of complex faults such as timing violations, power inefficiencies, and thermal issues. A detailed case study, conducted via a large-scale trace-driven co-simulation of a smart city environment, demonstrates the framework’s practical efficacy: by analyzing simulated temperature sensor telemetry and Field-Programmable Gate Array (FPGA) configurations, Sem4EDA identified specific energy inefficiencies and overheating risks, leading to actionable optimization strategies that resulted in a 23.7% reduction in power consumption and 15.6% decrease in operating temperature for the modeled sensor cluster. This work establishes a foundational step towards more autonomous, resilient, and semantically-aware hardware design and management systems.

Abstract: Aquaculture is a major contributor to Bangladesh’s economy, but farmers still struggle to maintain proper water quality because manual testing is slow, inaccurate, and difficult to manage in rural areas. This project introduces a low-cost, real-time monitoring system using an ESP32-32 N4 with temperature, pH, and TS300B turbidity sensors. The system collects water quality data and sends it to a mobile device, allowing farmers to track pond conditions remotely and receive alerts when values cross safe limits. Field tests show that the system provides reliable readings and helps reduce fish mortality by enabling quick action. This approach offers an affordable and practical solution for small-scale farmers, supporting better farm management and promoting wider technological adoption in the aquaculture sector.

Abstract: Digital product passports outline information about a product’s lifecycle, circularity, and sustainability related data. Sustainability data contains claims about carbon footprint, recycled material composition, ethical sourcing of production materials, etc. Also, upcoming regulatory directives require companies to disclose this type of information. However, current sustainability reporting practices face challenges, such as greenwashing, where companies make incorrect claims that are difficult to verify. There is also a challenge of disclosing sensitive production information when other stakeholders, such as consumers or other economic operators, wish to independently verify sustainability claims. Zero-knowledge proofs (ZKPs) provide a cryptographic system for verifying statements without revealing sensitive information. The goal of this research paper is to explore ZKP cryptography, trust models, and implementation concepts for extending DPP capability in privacy-aware reporting and verification of sustainability claims in products. To achieve this goal, first, formal representations of sustainability claims are provided. Then, a data matrix and trust model for the proof generation are developed. An interaction sequence is provided to show different components for various proof generation and verification scenarios for sustainability claims. Lastly, the paper provides a circuit template for the proof generation of an example claim and a credential structure for their input data validation.

Abstract:

This study addresses the persistent inefficiencies in incident management within regional public institutions, where dispersed offices and limited digital infrastructure constrain timely technical support. The research aims to evaluate whether a hybrid web architecture integrating AI-assisted interaction and mobile notifications can significantly improve efficiency in this context. The system was designed using a Laravel 10 MVC backend, a responsive Bootstrap 5 interface, and a relational MariaDB/MySQL model optimized with migrations and composite indexes, and incorporated two low-cost integrations: a stateless AI chatbot through the OpenRouter API and asynchronous mobile notifications using the Telegram Bot API managed via Laravel Queues and webhooks. Developed through four Scrum sprints and deployed on an institutional XAMPP environment, the solution was evaluated from January to April 2025 with 100 participants using operational metrics and the QWU usability instrument. Results show a reduction in incident resolution time from 120 to 31 minutes (74.17%), an 85.48% chatbot interaction success rate, a 94.12% notification open rate, and a 99.34% incident resolution rate, alongside an 88% usability score. These findings indicate that a modular, low-cost, and scalable architecture can effectively strengthen digital transformation efforts in the public sector, especially in regions with resource and connectivity constraints.

Abstract: The research investigates secure recommender systems through federated learning on educational platforms because online education platforms face increasing threats to student data privacy. The research creates an innovative system which merges FL technology with collaborative filtering to generate personalised course recommendations while maintaining user data protection on client devices. The system evaluated its performance by analysing data from major platforms including edX and Coursera and Udemy and other platforms through MSE and R-squared and precision and recall and F1-score metrics. The evaluation shows that FL maintains user privacy through data aggregation restrictions but users must accept reduced recommendation quality than what centralised systems offer. The research establishes two essential findings which confirm FL maintains user privacy in secure educational settings and reveals that performance reduction from limited data constitutes a core challenge for distributed systems. The research presents two primary methodological contributions which integrate data preprocessing methods for dealing with missing information and develop a complete evaluation system for federated recommendation platforms. The research results differ from previous studies because they demonstrate how model performance deteriorates when operating under federated system constraints. The research develops educational technology FL application expertise by studying privacy-accuracy tradeoffs and presenting methods to boost federated recommender systems in protected data environments.