Aluminum matrix composites (AMCs) have garnered significant attention across various industrial sectors owing to their remarkable properties compared to conventional engineering materials. These include low density, high strength-to-weight ratio, excellent corrosion resistance, enhanced wear resistance, and favorable high-temperature properties. These materials find extensive applications in the military, automotive, and aerospace industries. AMCs are manufactured using diverse processing techniques, tailored to their specific classifications. Over three decades of intensive research have yielded numerous scientific revelations regarding the internal and extrinsic influences of ceramic reinforcement on the mechanical, thermomechanical, tribological, and physical characteristics of AMCs. In recent times, AMCs have witnessed a surge in usage across high-tech structural and functional domains, encompassing sports and recreation, automotive, aerospace, defense, and thermal management applications. Notably, studies on particle-reinforced cast AMCs originated in India during the 1970s, attained industrial maturity in developed nations, and are now progressively penetrating the mainstream materials arena. This study provides a comprehensive understanding of AMC material systems, encompassing processing, microstructure, characteristics, and applications, with the latest advancements in the field.
Tanja E. J. Vos, Tijs van der Storm, Alexander Serebrenik
et al.
Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on advances in software engineering, yet the field is too often treated as a supportive digital component rather than as a strategic, enabling discipline. In policy frameworks, including major European programmes, software appears primarily as a building block within other technologies, while the scientific discipline of software engineering remains largely absent. This position paper argues that the long-term sustainability, dependability, and sovereignty of digital technologies depend on investment in software engineering research. It is a call to reclaim the identity of software engineering.
PURPOSE OR GOAL: This study investigates how GenAI can be integrated with a criterion-referenced grading framework to improve the efficiency and quality of grading for mathematical assessments in engineering. It specifically explores the challenges demonstrators face with manual, model solution-based grading and how a GenAI-supported system can be designed to reliably identify student errors, provide high-quality feedback, and support human graders. The research also examines human graders' perceptions of the effectiveness of this GenAI-assisted approach. ACTUAL OR ANTICIPATED OUTCOMES: The study found that GenAI achieved an overall grading accuracy of 92.5%, comparable to two experienced human graders. The two researchers, who also served as subject demonstrators, perceived the GenAI as a helpful second reviewer that improved accuracy by catching small errors and provided more complete feedback than they could manually. A central outcome was the significant enhancement of formative feedback. However, they noted the GenAI tool is not yet reliable enough for autonomous use, especially with unconventional solutions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY: This study demonstrates that GenAI, when paired with a structured, criterion-referenced framework using binary questions, can grade engineering mathematical assessments with an accuracy comparable to human experts. Its primary contribution is a novel methodological approach that embeds the generation of high-quality, scalable formative feedback directly into the assessment workflow. Future work should investigate student perceptions of GenAI grading and feedback.
Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>γ</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula>) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling.
The paper entitled "Qualitative Methods in Empirical Studies of Software Engineering" by Carolyn Seaman was published in TSE in 1999. It has been chosen as one of the most influential papers from the third decade of TSE's 50 years history. In this retrospective, the authors discuss the evolution of the use of qualitative methods in software engineering research, the impact it's had on research and practice, and reflections on what is coming and deserves attention.
Foundation models (FMs), particularly large language models (LLMs), have shown significant promise in various software engineering (SE) tasks, including code generation, debugging, and requirement refinement. Despite these advances, existing evaluation frameworks are insufficient for assessing model performance in iterative, context-rich workflows characteristic of SE activities. To address this limitation, we introduce \emph{SWE-Arena}, an interactive platform designed to evaluate FMs in SE tasks. SWE-Arena provides a transparent, open-source leaderboard, supports multi-round conversational workflows, and enables end-to-end model comparisons. The platform introduces novel metrics, including \emph{model consistency score} that measures the consistency of model outputs through self-play matches, and \emph{conversation efficiency index} that evaluates model performance while accounting for the number of interaction rounds required to reach conclusions. Moreover, SWE-Arena incorporates a new feature called \emph{RepoChat}, which automatically injects repository-related context (e.g., issues, commits, pull requests) into the conversation, further aligning evaluations with real-world development processes. This paper outlines the design and capabilities of SWE-Arena, emphasizing its potential to advance the evaluation and practical application of FMs in software engineering.
Marc Bruni, Fabio Gabrielli, Mohammad Ghafari
et al.
Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code security. Our benchmark leverages two peer-reviewed prompt datasets and employs static scanners to evaluate code security at scale. We tested multiple prompt engineering techniques on GPT-3.5-turbo, GPT-4o, and GPT-4o-mini. Our results show that for GPT-4o and GPT-4o-mini, a security-focused prompt prefix can reduce the occurrence of security vulnerabilities by up to 56%. Additionally, all tested models demonstrated the ability to detect and repair between 41.9% and 68.7% of vulnerabilities in previously generated code when using iterative prompting techniques. Finally, we introduce a "prompt agent" that demonstrates how the most effective techniques can be applied in real-world development workflows.
Muhammad Tayyab Khan, Zane Yong, Lequn Chen
et al.
Accurate extraction of key information from 2D engineering drawings is crucial for high-precision manufacturing. Manual extraction is slow and labor-intensive, while traditional Optical Character Recognition (OCR) techniques often struggle with complex layouts and overlapping symbols, resulting in unstructured outputs. To address these challenges, this paper proposes a novel hybrid deep learning framework for structured information extraction by integrating an Oriented Bounding Box (OBB) detection model with a transformer-based document parsing model (Donut). An in-house annotated dataset is used to train YOLOv11 for detecting nine key categories: Geometric Dimensioning and Tolerancing (GD&T), General Tolerances, Measures, Materials, Notes, Radii, Surface Roughness, Threads, and Title Blocks. Detected OBBs are cropped into images and labeled to fine-tune Donut for structured JSON output. Fine-tuning strategies include a single model trained across all categories and category-specific models. Results show that the single model consistently outperforms category-specific ones across all evaluation metrics, achieving higher precision (94.77% for GD&T), recall (100% for most categories), and F1 score (97.3%), while reducing hallucinations (5.23%). The proposed framework improves accuracy, reduces manual effort, and supports scalable deployment in precision-driven industries.
Shavindra Wickramathilaka, John Grundy, Kashumi Madampe
et al.
The use of diverse mobile applications among senior users is becoming increasingly widespread. However, many of these apps contain accessibility problems that result in negative user experiences for seniors. A key reason is that software practitioners often lack the time or resources to address the broad spectrum of age-related accessibility and personalisation needs. As current developer tools and practices encourage one-size-fits-all interfaces with limited potential to address the diversity of senior needs, there is a growing demand for approaches that support the systematic creation of adaptive, accessible app experiences. To this end, we present AdaptForge, a novel model-driven engineering (MDE) approach that enables advanced design-time adaptations of mobile application interfaces and behaviours tailored to the accessibility needs of senior users. AdaptForge uses two domain-specific languages (DSLs) to address age-related accessibility needs. The first model defines users' context-of-use parameters, while the second defines conditional accessibility scenarios and corresponding UI adaptation rules. These rules are interpreted by an MDE workflow to transform an app's original source code into personalised instances. We also report evaluations with professional software developers and senior end-users, demonstrating the feasibility and practical utility of AdaptForge.
The aim of this study was to investigate the impact of eBPF technology on the performance of network solutions in Kubernetes clusters. Two configurations were compared: a traditional iptables-based setup and eBPF based solution via the Cilium networking plugin. Performance tests were conducted, measuring throughput, latency, CPU usage, and memory consumption under unloaded and loaded conditions. The results indicate that the traditional configuration achieved higher throughput and lower latency in unloaded scenarios. However, under load, the eBPF-enabled cluster demonstrated advantages, including reduced CPU and memory usage and slightly improved latency. This study highlights the potential of eBPF as an efficient technology for Kubernetes environments, particularly in scenarios demanding high performance and resource efficiency.
Information technology, Electronic computers. Computer science
In the 21st century, digital technology has become integral to daily life, significantly impacting the skills and knowledge of undergraduate students. This research aims to develop a Semantic Web for learning digital technology in the 21st century by employing ontology techniques to enhance the efficiency of information retrieval. The system is designed to offer flexible learning, adaptable to students' needs, and focuses on categorizing content into three main classes and twelve subclasses. These classes define relationships using four object properties to connect main classes, subclasses, and instances, and four data type properties to link instances with data and relationships between digital technologies. This approach clarifies information and makes it more relevant for undergraduate students. Despite the advantages of ontology techniques in improving information retrieval and recommendation processes, challenges remain due to the complexity of constructing data relationships and establishing rules for data storage and retrieval. Effectively managing semantic data requires specialized knowledge to ensure accurate and efficient outcomes. The ontology knowledge base primarily consists of digital technology, innovation, and digital skills. Based on evaluations by three experts, the Semantic Web for digital technology learning in the 21st century, developed using ontology techniques, was rated at a very good level (\bar{x} = 4.52, S.D. = 0.19). The system's performance was also validated, showing precision at 96.25%, recall at 92.08%, and an F-measure of 95.29%, indicating its effectiveness in supporting learning through digital technology.
Problem statement. The integration of artificial intelligence (AI) into the field of education has become one of the key factors transforming pedagogical activities worldwide. The proliferation of generative AI tools (ChatGPT, DeepSeek, GigaChat) is accompanied by numerous discussions about their impact on the learning process and teachers’ professional activities. Among the main challenges highlighted in the global academic literature are: 1) the lack of unified attitudes towards AI use; 2) insufficient digital literacy among participants in the educational process; and 3) ethical and long-term risks of applying AI in education. The aim of this study is to explore future teachers’ attitudes towards the use of generative AI in solving professional tasks and to determine the impact of additional training on their perception of AI tools. Methodology. The empirical study involved 32 students pursuing a pedagogical profile. Surveys were conducted before and after completing an elective course on the use of AI in teachers’ professional activities. Methods included self-assessment (attitude survey), analysis of survey data, and statistical processing of results using the Student’s t-test to assess the significance of changes in future teachers’ attitudes towards AI. Results. The significance of additional training for improving future teachers’ attitudes towards AI has been confirmed. It was found that generative AI is perceived most positively in text generation tasks, while tasks involving assignment grading and generating video and audio materials inspire the least trust. The training helped reduce negative perceptions and improved the attitude towards using AI in solving professional tasks. Conclusion. The findings confirm the need for targeted training for future teachers in the fundamentals of AI to minimize negative aspects and ensure effective use of the technology. The developed principles could form the basis for creating educational disciplines and professional development courses, enabling more rational and safe applications of AI in education.
Daniel Cristóbal Andrade-Girón, Juana Sandivar-Rosas, William Joel Marin-Rodriguez
et al.
Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost, when applied to a clinical dataset comprising patients with CVD. The methodology entailed data preprocessing and cross-validation to regulate generalization. The performance of the model was evaluated using a variety of metrics, including accuracy, <i>F</i>1 score, precision, recall, Cohen’s Kappa, and area under the curve (<i>AUC</i>). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy ± SD: 93.36% ± 0.22; <i>F</i>1 score: 0.936; <i>AUC</i>: 0.9686). It also reached the lowest average rank (1.0) in Friedman test and was placed, together with Extra Trees (accuracy ± SD: 90.76% ± 0.18; <i>F</i>1 score: 0.916; <i>AUC</i>: 0.9689), in the superior statistical group (group A) according to Nemenyi post hoc test. The two models demonstrated a high degree of agreement with the actual labels (Kappa: 0.87 and 0.83, respectively), thereby substantiating their reliability in authentic clinical contexts. The findings substantiated the preeminence of aggregation-based ensemble methods in terms of accuracy, stability, and concordance. This underscored the prominence of Bagging and Extra Trees as optimal candidates for cardiovascular diagnostic support systems, where reliability and generalization were paramount.
Eduard C. Groen, Kazi Rezoanur Rahman, Nikita Narsinghani
et al.
The farming domain has seen a tremendous shift towards digital solutions. However, capturing farmers' requirements regarding Digital Farming (DF) technology remains a difficult task due to domain-specific challenges. Farmers form a diverse and international crowd of practitioners who use a common pool of agricultural products and services, which means we can consider the possibility of applying Crowd-based Requirements Engineering (CrowdRE) for DF: CrowdRE4DF. We found that online user feedback in this domain is limited, necessitating a way of capturing user feedback from farmers in situ. Our solution, the Farmers' Voice application, uses speech-to-text, Machine Learning (ML), and Web 2.0 technology. A preliminary evaluation with five farmers showed good technology acceptance, and accurate transcription and ML analysis even in noisy farm settings. Our findings help to drive the development of DF technology through in-situ requirements elicitation.
Aline de Campos, Jorge Melegati, Nicolas Nascimento
et al.
Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.
Abstract Digital Engineering is an emerging trend and aims to support engineering design by integrating computational technologies like design automation, data science, digital twins, and product lifecycle management. To enable alignment of industrial practice with state of the art, an industrial survey is conducted to capture the status and identify obstacles that hinder implementation in the industry. The results show companies struggle with missing know-how and available experts. Future work should elaborate on methods that facilitate the integration of Digital Engineering in design practice.
Sebastián Pizard, Diego Vallespir, Barbara Kitchenham
Context: Evidence-based software engineering (EBSE) can be an effective resource to bridge the gap between academia and industry by balancing research of practical relevance and academic rigor. To achieve this, it seems necessary to investigate EBSE training and its benefits for the practice. Objective: We sought both to develop an EBSE training course for university students and to investigate what effects it has on the attitudes and behaviors of the trainees. Method: We conducted a longitudinal case study to study our EBSE course and its effects. For this, we collect data at the end of each EBSE course (2017, 2018, and 2019), and in two follow-up surveys (one after 7 months of finishing the last course, and a second after 21 months). Results: Our EBSE courses seem to have taught students adequately and consistently. Half of the respondents to the surveys report making use of the new skills from the course. The most-reported effects in both surveys indicated that EBSE concepts increase awareness of the value of research and evidence and EBSE methods improve information gathering skills. Conclusions: As suggested by research in other areas, training appears to play a key role in the adoption of evidence-based practice. Our results indicate that our training method provides an introduction to EBSE suitable for undergraduates. However, we believe it is necessary to continue investigating EBSE training and its impact on software engineering practice.