The heterogeneity in the organization of software engineering (SE) research historically exists, i.e., funded research model and hands-on model, which makes software engineering become a thriving interdisciplinary field in the last 50 years. However, the funded research model is becoming dominant in SE research recently, indicating such heterogeneity has been seriously and systematically threatened. In this essay, we first explain why the heterogeneity is needed in the organization of SE research, then present the current trend of SE research nowadays, as well as the consequences and potential futures. The choice is at our hands, and we urge our community to seriously consider maintaining the heterogeneity in the organization of software engineering research.
The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.
Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.
Gamification in education has gained increasing popularity as a means to promote quality education, particularly when integrated with mobile technology to create engaging and accessible learning environments that align with technological advancements. However, a more comprehensive understanding is needed regarding students’ acceptance of such technology, especially in the context of the electrical machinery course. This study investigates students’ acceptance of mobile-based gamified learning (MoGaLearn) in the instruction of electrical machinery. Student acceptance is assessed through the actual use (AU) variable, based on the technology acceptance model (TAM) framework. The theoretical model employed includes key determinants such as perceived ease of use (PEU), perceived usefulness (PU), attitude toward use (A), and behavioral intention (BI). A quantitative, survey-based approach was adopted, involving 136 engineering students who completed a structured questionnaire. Data were analyzed using variance-based structural equation modeling (VB-SEM). The findings reveal that students of the Industrial Electrical Engineering program demonstrate a high level of acceptance toward MoGaLearn in the electrical machinery course. The constructs PEU, PU, A, and BI were empirically found to have a positive and significant influence on students’ acceptance of MoGaLearn. These results highlight the importance of considering these factors in the design, development, selection, and implementation of gamification learning tools in engineering education.
Helical springs are used for many mechanisms. Rectangular wire helical springs are used in machines that require large spring loads, such as press machines, die machines, injection molding machines, construction machines, and load testing machines. Design formulas for the rectangular wire helical springs were given by Liesecke. However, pitch angle of the helical spring is neglected in his formulas, and they are inconvenient because we have to read factors used in the formulas from graphs. And, Shimizu et al. derived a theoretical equation, but there are still differences between values calculated by the equations and the FEM analysis results although a trend is consistent. And, the practical design equations are desired to be simple. Therefore, in this paper, simple practical design equations of the spring constant and the maximum shear stress are derived by using a fractional expression to FEM results by focusing on that the displacement and the stress generated in the helical spring are mainly caused by a tortional moment to the spring wire. Errors of the spring constant equations to the FEM results are less than 3 percents and errors of the maximum shear stress equation to the FEM results are less than 3.5 percents. Therefore, these equations are very useful for the practical design of the rectangular wire helical springs.
Mechanical engineering and machinery, Engineering machinery, tools, and implements
In this study, as part of the development of a generic track structure design method, a method for verifying the fatigue life of rails on curves was investigated. To determine the design force for verifying the fatigue life of rails on curves, the lateral force during a vehicle run was estimated by the wheel / lateral force estimation formula by varying various parameters such as the curve radius. Taking a curve segment 800 m > R ≥ 600 m as an example, it was assumed that curves of the same extension existed for each curve radius and superelevation, and the probability frequency distribution of the variable lateral force coefficient was determined. Then, all curves on all lines laid by a certain operator were classified into three curve categories, (i) R ≥ 800 m, (ii) 800 m > R ≥ 600 m and (iii) 600 m > R. The total curve length was classified into each category, and the probability of occurrence of the lateral force was determined. When the mean and standard deviation coefficient of variation of the lateral force coefficient was calculated for the results, it was 0.25 for (i) R ≥ 800 m, 0.30 for (ii) 800 m > R ≥ 600 m and 0.35 for (iii) 600 m > R. Then, methods for estimating the bending stress at the bottom of the rail under lateral force, which is the response value, were investigated. A FEM model of both rail tracks was constructed considering vertical and horizontal bending and torsional bending, nonlinearity of the fastening systems, and lateral force on the high and low rails. The results of the analysis using this model confirmed that the bending stresses at the bottom of the rails can be estimated to be within 10 % accuracy at the bottom of the rails during vehicle passage on the service line.
Mechanical engineering and machinery, Engineering machinery, tools, and implements
The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.
Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy
et al.
Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.
The rapid emergence of generative AI models like Large Language Models (LLMs) has demonstrated its utility across various activities, including within Requirements Engineering (RE). Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses. However, existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities. The objective of this study is to explore the applicability of existing prompt engineering guidelines for the effective usage of LLMs within RE. To achieve this goal, we began by conducting a systematic review of primary literature to compile a non-exhaustive list of prompt engineering guidelines. Then, we conducted interviews with RE experts to present the extracted guidelines and gain insights on the advantages and limitations of their application within RE. Our literature review indicates a shortage of prompt engineering guidelines for domain-specific activities, specifically for RE. Our proposed mapping contributes to addressing this shortage. We conclude our study by identifying an important future line of research within this field.
Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, commonality, fix effort, fix strategies, and temporal evolution. Our findings reveal six bug symptom types and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers, along with general guidelines for developing LLM inference engines.
Sentiment analysis is an essential technique for investigating the emotional climate within developer teams, contributing to both team productivity and project success. Existing sentiment analysis tools in software engineering primarily rely on English or non-German gold-standard datasets. To address this gap, our work introduces a German dataset of 5,949 unique developer statements, extracted from the German developer forum Android-Hilfe.de. Each statement was annotated with one of six basic emotions, based on the emotion model by Shaver et al., by four German-speaking computer science students. Evaluation of the annotation process showed high interrater agreement and reliability. These results indicate that the dataset is sufficiently valid and robust to support sentiment analysis in the German-speaking software engineering community. Evaluation with existing German sentiment analysis tools confirms the lack of domain-specific solutions for software engineering. We also discuss approaches to optimize annotation and present further use cases for the dataset.
Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén
et al.
The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.
The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is revolutionising agricultural engineering systems by enabling data-driven, automated, and highly efficient farm management practices. AI algorithms, including machine learning, deep learning, and computer vision, are increasingly applied to solve complex problems in crop monitoring, yield prediction, disease detection, and decision-making. IoT technologies, comprising sensor networks, wireless communication, and real-time monitoring tools, facilitate continuous data acquisition from soil, crops, climate, and machinery. When integrated, these technologies form the Artificial Intelligence of Things (AIoT), a smart agricultural framework capable of autonomous responses, predictive analytics, and resource optimisation. This review explored the roles, applications, benefits, emerging trends, and challenges associated with AI and IoT in agricultural engineering, offering a comprehensive understanding of how digital transformation is shaping the future of agriculture. AIoT systems are reshaping traditional farming by offering precision irrigation, livestock monitoring, pest control, and automated machinery operations, significantly improving productivity, reducing input costs, and supporting sustainable practices. Recent advances such as edge computing, blockchain, digital twins, and drone-based imaging are further enhancing real-time data processing, traceability, and simulation capabilities. These innovations are helping address global challenges such as food security, water scarcity, and climate change. Despite these advancements, several challenges persist, including poor rural connectivity, high implementation costs, lack of interoperability, data privacy concerns, and limited technical expertise among farmers. Overcoming these limitations requires multi-stakeholder collaboration, investment in rural infrastructure, standardisation of digital platforms, and targeted training programs. The adoption of AI and IoT in agriculture is rapidly increasing, driven by research breakthroughs, startup ecosystems, and supportive policy frameworks. As these technologies continue to evolve, their integration will be central to building smart, resilient, and climate-adaptive agricultural systems capable of meeting the demands of a growing global population.
The reliability of ship machinery is a critical factor in marine engineering, where failures can lead to costly downtime and safety risks. Traditional maintenance approaches are based on periodic inspections and manual documentation, which are often inefficient and prone to errors. Recent advances in digital technologies enable the development of web-based solutions that improve accessibility, data integrity, and decisionmaking in maintenance processes. This paper presents the design and implementation of eShipMND, a web-based system for condition monitoring and maintenance management of marine mechanisms. The system integrates a structured database with modules for monitoring, reporting, spare parts management, and task scheduling. A user-friendly interface provides access control, data visualization, and automated generation of maintenance reports. The architecture allows easy integration with additional modules, including machine learning tools for predictive diagnostics. The proposed solution enhances maintenance efficiency, reduces downtime, and provides a cost-effective alternative to traditional industrial SCADA packages, offering significant benefits for the maritime sector.
Introduction. The agricultural machinery market constitutes one of the key segments of the agro-industrial complex, as its development directly determines the level of technological efficiency of agricultural production, labour productivity, and the competitiveness of the agrarian sector in both domestic and international markets. An important direction of such optimization is clustering – an innovative form of organizing production and market interaction based on the integration of machinery manufacturers, research institutes, service companies, dealers, and end users into a unified system of cooperation. This model enhances cooperation, supports knowledge and technology exchange, reduces transaction costs, and stimulates the development of domestic production of technical equipment. Methodology. The study applies a systemic approach that allows examining the clustering of the agricultural machinery market as a holistic phenomenon, taking into account economic, organizational, and institutional factors. Comparative analysis was used to juxtapose European and Ukrainian experiences in cluster development. The abstract–logical method was applied to formulate theoretical concepts and the conceptual foundations of clustering. In addition, structural–functional analysis was used to determine the role of clusters in strengthening the competitiveness of Ukraine’s agricultural machinery market. Results. The research substantiates that clustering is an effective tool for enhancing the competitiveness of Ukraine’s agricultural machinery market under the conditions of European integration. It was identified that cluster formation contributes to the optimization of production and logistics processes, cost reduction, and intensification of innovation activity. The findings demonstrate that integration of enterprises into clusters enables more efficient attraction of investment and international financial resources. Furthermore, it is established that successful implementation of the cluster model requires strengthened institutional support and alignment with EU policies in the field of agricultural engineering.
The Federal project “System Measures for Increase in Labor Productivity” is a key element of the national project “Labor Productivity”. Its main goal is to create conditions for sustainable growth in labor productivity in the real economy sector. As a part of the project implementation, enterprises receive qualified expert support in implementing lean production methodology, optimizing business processes and technological operations. Special attention is paid to staff training in modern methods of production process management. The article presents a detailed analysis of the practical experience in implementing measures to increase operational efficiency at a large machine-building enterprise - the leader in domestic railway machinery manufacturing. Methods for production problem area identification, developed solutions, and achieved results are described. Both managerial and technical aspects of production process modernization are considered. Particular attention is given to adapting lean production methodology for machine-building production with small product batches. The stages of program implementation are described in detail - from initial diagnostics to the introduction of specific solutions. The characteristics of applying lean production concepts in combination with the Theory of Constraints tools to solve specific machine-building production challenges are analyzed. The results of implementing shift/day task systems, methods for balancing production capacities, and technical re-equipment of the enterprise are presented. The achieved results demonstrate the possibility of significant improvement in operational efficiency through a comprehensive approach to production process optimization. Indicators of increased production volume, reduced production cycle time, and decreased inventory levels confirm the effectiveness of the applied methods. This article will be useful for industrial enterprise managers, production organization specialists, and lean production experts interested in the practical aspects of implementing modern production management methodologies.
William Manjud Maluf Filho, Sofia Lucas Yoshimura, Ana Marques
et al.
The continuous pursuit of operational excellence in the tire manufacturing industry necessitates structured approaches to minimize production defects, improve resource utilization, and enhance product reliability. This study presents a comprehensive case study focused on the implementation of Lean Manufacturing tools within a high-volume production facility specialized in truck and bus radial (TBR) tires. The production line under investigation exhibited recurring defects on the sidewall region of the cured tires, referred to as defect F1, stemming primarily from condensation phenomena and steam management inefficiencies during the curing process. A detailed root cause analysis was conducted through structured brainstorming sessions, Ishikawa diagrams, and the 5 Whys method, revealing multiple converging causes including excessive internal pressure, improper drainage angles, degraded sealing interfaces, and inadequate vapor shielding. In response, a corrective action plan was deployed, integrating the installation of pneumatic OR-valves, realignment of machinery to induce gravitational drainage, application of protective skirts, and the adoption of preventive maintenance protocols. The interventions yielded a defect reduction from an average of 3.0% to 1.4%, representing a 39% improvement, well above the initial target of 20%. Additionally, a 30-second cycle time reduction was achieved, enabling an incremental output of 1,358 tires over a 23-day observation window and translating into more than 390 hours of direct labor saved. These results underscore the effectiveness of integrating Lean principles such as Kaizen, Single Minute Exchange of Die (SMED), and 5S within the framework of industrial tire manufacturing. The study not only validates the technical efficacy of the proposed interventions but also highlights the strategic value of Lean-based quality engineering for driving competitiveness in automotive component production.
Megan E Salwei, Barbara Voigtman, Janelle Faiman
et al.
Introduction: As the number of available treatment options for breast cancer increases, decision-making for patients has become complex. Patients often struggle to make decisions as treatment options can vary in terms of short- and long-term side effects, risks of recurrence, and impact on daily life.1 Numerous decision aids have been developed to support patient decision-making.2 However, sustained implementation and use of these tools remains limited. We propose that cognitive engineering approaches, such as naturalistic decision making (NDM), can provide a deeper understanding of how patients make treatment decisions, which can improve the design of decision support tools. Naturalistic decision making (NDM) is a theoretical perspective and methodological approach used to understand how people make decisions in the real world. Originally developed to understand decision-making of expert firefighters during crises, NDM approaches have been used to understand complex decision-making across domains including the military, offshore oil rigs, and healthcare.3,4 In this study, we used an NDM approach, the critical decision method (CDM), to gain an in-depth understanding of how breast cancer patients make treatment decisions following diagnosis. Methods: We conducted CDM interviews,5 with breast cancer patients diagnosis in the last 12 years. CDM interviews aim to understand critical or difficult events by unpacking the event using structured probes. One researcher conducted each interview over Zoom. We started each interview by asking the patient to reflect back on the beginning of their cancer journey and what they remember about their diagnosis. We then drew a timeline and asked the patient to relay the different treatments they considered or underwent for breast cancer. We then asked “Can you think of a time during your breast cancer journey when you had to make a difficult decision?” and probed patients about that decision. We continued asking patients about their treatment decision-making as time allowed. Each interview was audio-recorded and transcribed. A researcher and a patient advocate coded each interview and created a decision requirements table,6 which detailed the decisions made by the patient, what made that decision challenging, what strategies and information they used, and what their goals were at the time. We then met to discuss and come to consensus. Once a decision requirements table was created for each transcript, we developed aggregate tables and identified key themes. Results: We conducted 20 interviews, averaging 57 minutes each; patient age ranged from 42 to 81 years. Patients described an average of 8 decisions that they made following breast cancer diagnosis. Despite many patients facing the same decisions (e.g., mastectomy vs. lumpectomy), we found variability in which decisions were most difficult for patients. We identified 11 categories of difficult decisions for patients including whether to receive chemotherapy, getting genetic testing, stopping a medication due to side effects, and deciding where to receive treatment. Patients reported feeling time pressure and urgency to make treatment decisions and a fear of regretting their decisions. We found that patients’ firsthand experiences from friends who had cancer influenced their treatment decision-making. Given the heterogeneous nature of breast cancer treatment, this often presented a barrier to decision-making as patients expected to have the same experience and treatment options as their friends. Patients expressed variable goals when making treatment decisions, which often changed throughout their treatment journey. Conclusion: In this study, we explored how breast cancer patients made treatment decisions using NDM methods. This cognitive engineering approach revealed intricacies in the decision-making process of patients that will be valueable for improving the design of decision support tools. Next steps include collaborative design with patients to develop a tool that supports the broad spectrum of treatment decisions made across the patient journey. Citation Format: Megan Salwei, Barbara Voigtman, Janelle Faiman, Carrie Reale, Shilo Anders, Matthew Weinger. Harnessing Cognitive Engineering to Understand Breast Cancer Patient Decision Making [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-04-07.