This study introduces a framework that leverages the synergistic potential of Virtual Reality (VR) and Machine Learning (ML) to enhance graphical modeling in engineering and architectural design. Traditional clash detection methods in Building Information Modeling (BIM) systems are predominantly reactive, identifying discrepancies only after their occurrence, leading to costly and time-consuming design revisions. By integrating ML algorithms with VR-driven BIM, our approach proactively identifies and resolves clashes, as demonstrated across 28 diverse engineering projects. The results indicate a reduction in design clashes by 16% and iterative revisions by 15%, culminating in a 12% decrease in overall project timelines. This research underscores the transformative impact of combining VR and ML on additive manufacturing (AM) workflows, significantly improving efficiency and reducing the iterative nature of traditional methods. The findings highlight the framework’s scalability and adaptability, promising substantial advancements in engineering and architecture practices.
Federica Del Carlo, G. Bianchini, D. Altafini
et al.
ABSTRACT Italy’s high seismic activity poses significant challenges for its rich cultural heritage of historic religious buildings, which are often constructed with traditional techniques that offer limited seismic resistance and are rarely retrofitted. These structures have historically demonstrated high vulnerability to seismic events. Conventional structural engineering methods typically focus on a building’s capacity to withstand seismic actions, but such methods often overlook critical factors like evacuation and rescue operations. A comprehensive definition of vulnerability considers physical, social, economic, and environmental aspects, emphasising the importance of road-infrastructure resilience. This study proposes a novel risk matrix for the seismic evaluation of historical religious buildings, integrating hazard intensity with asset susceptibility. Asset susceptibility is defined by three factors: exposure, territorial exposure, and physical susceptibility. By applying this matrix to thirty-seven churches in the Tuscany region, the present study wants to highlight the role of road-network resilience when analysing the overall susceptibility of historical buildings to seismic hazards.
The Inelastic Acceleration Ratio (IAR) is a helpful instrument for determining the maximum inelastic acceleration from the related elastic acceleration that seems to have been little examined in past research. The IARs using single-degree-of-freedom (SDOF) systems with various structural factors under thirty pairs of ground motion earthquakes recorded are investigated in this paper. The linear elastic-perfect plastic model is used to model SDOF systems. The factors to consider include elastic vibration period (T), displacement ductility ratios (μ, 2-8), the post-yield stiffness ratio (α, 0-15%), and the damping ratio (x, 3-20%). The results showed that the IAR values are decreased with an increase in the ductility ratios (μ) while the IAR values are increased with an increase in the damping ratios (x). While the post-yield stiffness ratio (α) has little effect on the IAR. Also, Analytical formulae are used to estimate IAR based on the T, μ, α, and x.
Architectural engineering. Structural engineering of buildings, Structural engineering (General)
Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski
et al.
Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the wheel; every course is designed from scratch with little to no reuse of ideas or content across the community. Due to the nature of the topic, it is rather difficult to get it right the first time when defining the learning objectives, selecting the material, compiling a reader, and, more importantly, designing relevant and appropriate practical work. This leads to substantial effort (through numerous iterations) and poses risks to the course quality. This chapter attempts to support educators in the first and most crucial step in their course design: creating the syllabus. It does so by consolidating the collective experience of the authors as well as of members of the Empirical Software Engineering community; the latter was mined through two working sessions and an online survey. Specifically, it offers a list of the fundamental building blocks for a syllabus, namely course aims, course topics, and practical assignments. The course topics are also linked to the subsequent chapters of this book, so that readers can dig deeper into those chapters and get support on teaching specific research methods or cross-cutting topics. Finally, we guide educators on how to take these building blocks as a starting point and consider a number of relevant aspects to design a syllabus to meet the needs of their own program, students, and curriculum.
The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited by cognitive overload, inefficient tool integration, and the narrow capabilities of AI copilots. In response, we propose Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems as part of the emerging Software Engineering 3.0 era. Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution. This process involves dynamic optimization of cognitive architectures and their constituents (e.g., prompts, foundation model configurations, and system parameters) while finding the optimal trade-off between several objectives, such as accuracy, cost, and latency. This paper outlines the architecture of Compiler.next and positions it as a cornerstone in democratizing software development by lowering the technical barrier for non-experts, enabling scalable, adaptable, and reliable AI-powered software. We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search heuristics, reproducibility, and interoperability between compilers. Our vision lays the groundwork for fully automated, search-driven software development, fostering faster innovation and more efficient AI-driven systems.
Timo Kehrer, Robert Haines, Guido Juckeland
et al.
Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.
Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt
The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.
Daniel Mendez, Paris Avgeriou, Marcos Kalinowski
et al.
Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.
The carbon emissions of urban residential buildings are substantial. However, the standard operating conditions specified in current energy-saving standards significantly differ from the actual energy consumption under real operating conditions. Therefore, it is essential to consider the impact of residents’ actual energy consumption behavior in carbon emission forecasts. To improve the accuracy of carbon emission predictions for urban residential buildings, this paper focuses on residential buildings in Guangzhou. Taking into account the energy consumption behavior of residents, parameterized modeling is carried out in the R language, and simulation is carried out using EnergyPlus software. Analysis revealed that the higher the comfort level of residential energy consumption behavior, the more it is necessary to encourage residents to adopt energy-saving behaviors. Combining carbon emission factors, air-conditioning energy efficiency, and the power consumption models of lighting and electrical equipment, a comprehensive operational carbon emission prediction model for urban residential operations in Guangzhou was developed. By comparing the prediction model with an actual case, it was found that the prediction deviation was only 4%, indicating high accuracy. The proposed operational carbon emission model can quickly assist designers in evaluating the carbon emissions of urban residential buildings in the early stages of design, providing an accurate basis for decision-making.
In this work, a numerical study of a thermal performance of water flow inside a dimpled pipe. The effect of three types of dimples (circular, square and rhombus) studied in the numerical simulation. A commercial program called ANSYS was used to model the flow through a circular pipe .The three-dimensional governing differential equations of mass, momentum, and energy were used together with the (K − ε ) model to evaluate the impact of dimples on a turbulent flow and the velocity field. The study was carried out in the Reynolds number (Re) range (2500–15000). The research results demonstrate that the presence of a dimple on the pipe surface greatly increases the rate of heat transmission and the friction factor compared to a normal smooth pipe. Also, the numerical study demonstrated that the Nusselt number (Nu) in case of circular dimples at diameter (4 , 6 and 8) mm was (22, 28 and 43) % greater than the smooth surface. It is discovered that the improved pipe with circular dimples have a benefit for increased heat transmission efficiency compared with the square and rhombus dimples. Additionally , circular dimples have the ability to supply the lowest friction factor (f) when compared to other types of dimple. The pipe with circular dimples with D= 4mm , at Reynolds number 2500 provided the largest thermal performance criterion (PEC) value about 1.44.
The case study is a research approach carried out in a natural, holistic, and in-depth setting about a unique, best practice, and bounded phenomenon. Even though it was rooted in social sciences, the case study has characteristics that are almost the same as a process of studying past architectural works (precedent studies) that have long been carried out in architecture. Unfortunately, precedent studies are sometimes only limited to documentation and descriptions of architectural works as physical objects, without critical, in-depth and holistic analysis of the decisions behind the design decisions of a precedent. On the other hand, previous research that discussed the case study approach in architecture tended to understand this approach as just a social research method that was simply applied in architectural research. Therefore, it is crucial to understand the terms of the 'case' itself. This research aims to provide a complete and in-depth understanding of the case study approach, to filter and elaborate this approach for architectural research. This research examines and elaborates on credible and recent literature on case study, both in social and architectural research. Elaboration from the literature review is used to formulate an operational framework based on architectural function-form-meaning. It is hoped that the results of this research will enrich architectural knowledge. The in-depth understanding is ultimately beneficial for improving existing architectural practices and becoming a source of knowledge for the general public.
Architecture, Architectural engineering. Structural engineering of buildings
LinkedIn is the largest professional network in the world. As such, it can serve to build bridges between practitioners, whose daily work is software engineering (SE), and researchers, who work to advance the field of software engineering. We know that such a metaphorical bridge exists: SE research findings are sometimes shared on LinkedIn and commented on by software practitioners. Yet, we do not know what state the bridge is in. Therefore, we quantitatively and qualitatively investigate how SE practitioners and researchers approach each other via public LinkedIn discussions and what both sides can contribute to effective science communication. We found that a considerable proportion of LinkedIn posts on SE research are written by people who are not the paper authors (39%). Further, 71% of all comments in our dataset are from people in the industry, but only every second post receives at least one comment at all. Based on our findings, we formulate concrete advice for researchers and practitioners to make sharing new research findings on LinkedIn more fruitful.
Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.
Mehil B Shah, Mohammad Masudur Rahman, Foutse Khomh
Deep learning (DL) techniques have achieved significant success in various software engineering tasks (e.g., code completion by Copilot). However, DL systems are prone to bugs from many sources, including training data. Existing literature suggests that bugs in training data are highly prevalent, but little research has focused on understanding their impacts on the models used in software engineering tasks. In this paper, we address this research gap through a comprehensive empirical investigation focused on three types of data prevalent in software engineering tasks: code-based, text-based, and metric-based. Using state-of-the-art baselines, we compare the models trained on clean datasets with those trained on datasets with quality issues and without proper preprocessing. By analysing the gradients, weights, and biases from neural networks under training, we identify the symptoms of data quality and preprocessing issues. Our analysis reveals that quality issues in code data cause biased learning and gradient instability, whereas problems in text data lead to overfitting and poor generalisation of models. On the other hand, quality issues in metric data result in exploding gradients and model overfitting, and inadequate preprocessing exacerbates these effects across all three data types. Finally, we demonstrate the validity and generalizability of our findings using six new datasets. Our research provides a better understanding of the impact and symptoms of data bugs in software engineering datasets. Practitioners and researchers can leverage these findings to develop better monitoring systems and data-cleaning methods to help detect and resolve data bugs in deep learning systems.
Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Without that model connectivity, requirement quality can suffer due to imprecision and inconsistent terminology, frustrating communication during system development. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. The Model-Based Structured Requirement SysML Profile was extended to comply with the INCOSE Guide to Writing Requirements updated in 2023 while conforming to the ISO/IEC/IEEE 29148 standard requirement statement templates. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition and requirements V&V. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to explore its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support with the system architecture modeling software.
Today, the service of Voice over Internet Protocol or (VoIP) is one of the most used service around the globe in many fields, especially with video conferencing applications. Different applications and also many communications companies around the world use the VoIP service. Communication stakeholders aim to keep old customers happy and attract new customers. There was a need for some kind of service quality measures. Metrics are used to quantify the quality of transmission link are known Quality of Service (QoS). Whereas, user satisfaction level is identified by using Quality of Experience (QoE). This paper conducted a simulation model for VoIP service over heterogeneous, using OPNET modeler. This work aims to compare the user satisfaction level of the VoIP service by using different coding schemes in the application level of the User Equipment (UE). The measured QoS parameters over the heterogeneous transmission link were IP packet delay, IP Packet jitter, IP packet loss, MAC layer delay, PHY layer throughput in the network. Additionally, in this simulation model the use of E-Model to assess QoE level using Mean Opinion Score (MOS) metric in predicting the VoIP call quality. The results using the vocoder G.729A during the (30) sec of the voice call simulation time were as follows: Delay 0.57 mSec, Throughput of 290 Kbits/sec, Jitter of 0.02 uSec, MOS value of 3.76. The results using the vocoder G.711 during the same aforementioned time were as follows: Delay 0.25 mSec, Throughput of 70 Kbits/sec, Jitter of 0 Sec), MOS value of 3.08.
Christopher Fapohunda, O. E. Osanyinlokun, A. O. Abioye
The field of structural engineering has in recent times begun to widen its scope from the traditional analysis and design, into the development of new structural materials. This is because the use of non-renewable materials in forming and framing structural projects are raising serious environmental concerns bothering on sustainability of materials, especially cement, to produce structural concrete. Cement has been found to be a major contributor to greenhouse gases which affect the environment negatively. Waste from both the industrial and agricultural industries are gradually becoming sources of material to partly replace cement in concrete because of their pozzolanic properties. The agro-based pozzolanic materials include Rice husk Ash (RHA), Saw dust ash (SDA), Palm oil fuel ash (POFA) amongst others. To further widen the scope and resource base of pozzolanic materials for concreting, ternary blends consisting of agro-based pozzolans are being researched into. These research efforts however appear to be uncoordinated, and thus there is a need to juxtapose these efforts together to see the extent of work done on such ternary blends and present their relevant structural properties. This is with a view to helping identify gaps in such research as a means of preventing wastage of research energies. This paper presents a review of structural properties of some agro-based ternary blends used in structural concrete. It is concluded that more research effort is needed, especially in the development of practical and acceptable guidelines that will aid their application in concrete, for sustainable production of structural concrete.
Architectural engineering. Structural engineering of buildings, Structural engineering (General)
Agriculture has a good stake in the world’s GDP. In many countries, agriculture and allied sectors have a good stake in national GDP. This paper covers details related to livestock since 1960s. The workforce has managed livestock for many decades. The workforce increases as the number of animals increases; it is an energy, time-consuming, and economically costly approach. Apart from it, there is no assurance about animal welfare in case of diseases, breeding, and feed intake issues. In the 21st century of digitalization, technology has a key role in improving overall monitoring, controlling, and processing in livestock management. This paper has gone thoroughly into the manual and automated livestock farm management, aiming welfare of animals, livestock products, consumers’ benefit, and sustainable environmental approaches.
Kelechi G. Kalu, Taylor R. Schorlemmer, Sophie Chen
et al.
The primary theory of software engineering is that an organization's Policies and Processes influence the quality of its Products. We call this the PPP Theory. Although empirical software engineering research has grown common, it is unclear whether researchers are trying to evaluate the PPP Theory. To assess this, we analyzed half (33) of the empirical works published over the last two years in three prominent software engineering conferences. In this sample, 70% focus on policies/processes or products, not both. Only 33% provided measurements relating policy/process and products. We make four recommendations: (1) Use PPP Theory in study design; (2) Study feedback relationships; (3) Diversify the studied feedforward relationships; and (4) Disentangle policy and process. Let us remember that research results are in the context of, and with respect to, the relationship between software products, processes, and policies.