We explore the concept of folklore within software engineering, drawing from folklore studies to define and characterize narratives, myths, rituals, humor, and informal knowledge that circulate within software development communities. Using a literature review and thematic analysis, we curated exemplar folklore items (e.g., beliefs about where defects occur, the 10x developer legend, and technical debt). We analyzed their narrative form, symbolic meaning, occupational relevance, and links to knowledge areas in software engineering. To ground these concepts in practice, we conducted semi-structured interviews with 12 industrial practitioners in Sweden to explore how such narratives are recognized or transmitted within their daily work and how they affect it. Synthesizing these results, we propose a working definition of software engineering folklore as informally transmitted, traditional, and emergent narratives and heuristics enacted within occupational folk groups that shape identity, values, and collective knowledge. We argue that making the concept of software engineering folklore explicit provides a foundation for subsequent ethnography and folklore studies and for reflective practice that can preserve context-effective heuristics while challenging unhelpful folklore.
The seismic performance of buildings is a critical factor in structural engineering, especially in regions prone to earthquakes. Among various geometries, circular and rectangular buildings are widely adopted in architectural designs due to their functional and aesthetic appeal. This review paper provides an in-depth examination of the seismic behaviour of these two geometries using STAAD Pro, a widely used structural analysis and design software. Circular buildings exhibit distinct advantages in seismic resilience, such as uniform stress distribution and enhanced torsional resistance, stemming from their symmetric geometry. However, their structural complexity and higher construction costs pose significant challenges. Rectangular buildings, being simpler and more economical to construct, dominate urban landscapes but are often prone to stress concentration at corners and higher torsional vulnerability in asymmetric configurations. This paper explores various case studies and research findings to analyze the dynamic responses of these geometries under seismic loads using STAAD Pro simulations. Circular buildings demonstrate superior energy dissipation and reduced seismic drift, while rectangular buildings require additional structural interventions, such as shear walls and base isolation systems, to achieve comparable resilience. The review also emphasizes the importance of advanced modelling techniques in STAAD Pro to enhance the seismic performance of both geometries. Key findings from the comparative analysis underline the need for careful planning and innovative design solutions to address the inherent challenges associated with each shape. Furthermore, this paper identifies critical research gaps, such as the need for experimental validation and exploration of hybrid geometries, to bridge existing knowledge gaps and push the boundaries of seismic engineering. In conclusion, this review highlights the interplay between geometry, structural dynamics, and seismic resilience, offering valuable insights for architects, engineers, and researchers. By integrating state-of-the-art technologies, sustainable design approaches, and STAAD Pro’s capabilities, future advancements in seismic analysis can lead to the development of safer, cost-effective, and more resilient structures, ultimately mitigating the devastating impacts of earthquakes on the built environment.
This paper aims to solve the problems of extensive labor input and long construction periods of traditional foundation beam brick formwork. We propose using plant fiber-reinforced cementitious composites (PFRCC) panels instead of conventional foundation beam brick formwork. This study conducted tests and finite element analyses on the PFRCC panels employed as permanent formwork for foundation beams, based on a construction project in a community in Lanzhou City, Gansu Province, China. The results indicate that the maximum bending stress of the PFRCC panels utilized as permanent formwork for foundation beams is 0.4069 MPa, occurring at the junction between the side and bottom forms, which is significantly lower than the specified design strength of 13.7 MPa. Furthermore, the maximum deformation recorded was 1.528 mm at the mid-span of the side template, remaining below the permissible limit of 2.5 mm. The bending strength and stiffness deformation meet the design requirements, which shows that the PFRCC panels can be used as the permanent formwork of the foundation beam.
Architectural engineering. Structural engineering of buildings, Structural engineering (General)
Kevin Hermann, Sven Peldszus, Jan-Philipp Steghöfer
et al.
Software security is of utmost importance for most software systems. Developers must systematically select, plan, design, implement, and especially, maintain and evolve security features -- functionalities to mitigate attacks or protect personal data such as cryptography or access control -- to ensure the security of their software. Although security features are usually available in libraries, integrating security features requires writing and maintaining additional security-critical code. While there have been studies on the use of such libraries, surprisingly little is known about how developers engineer security features, how they select what security features to implement and which ones may require custom implementation, and the implications for maintenance. As a result, we currently rely on assumptions that are largely based on common sense or individual examples. However, to provide them with effective solutions, researchers need hard empirical data to understand what practitioners need and how they view security -- data that we currently lack. To fill this gap, we contribute an exploratory study with 26 knowledgeable industrial participants. We study how security features of software systems are selected and engineered in practice, what their code-level characteristics are, and what challenges practitioners face. Based on the empirical data gathered, we provide insights into engineering practices and validate four common assumptions.
Vladyslav Bulhakov, Giordano d'Aloisio, Claudio Di Sipio
et al.
The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for generating domain models from textual descriptions. We use search-based methods to tune hyperparameters for a specific medical data model, resulting in a notable quality improvement over the baseline LLM. We then test the optimized hyperparameters across ten diverse application domains. While the solutions were not universally applicable, we demonstrate that combining hyperparameter tuning with prompt engineering can enhance results across nearly all examined domain models.
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.
While mastered by some, good scientific writing practices within Empirical Software Engineering (ESE) research appear to be seldom discussed and documented. Despite this, these practices are implicit or even explicit evaluation criteria of typical software engineering conferences and journals. In this pragmatic, educational-first document, we want to provide guidance to those who may feel overwhelmed or confused by writing ESE papers, but also those more experienced who still might find an opinionated collection of writing advice useful. The primary audience we had in mind for this paper were our own BSc, MSc, and PhD students, but also students of others. Our documented advice therefore reflects a subjective and personal vision of writing ESE papers. By no means do we claim to be fully objective, generalizable, or representative of the whole discipline. With that being said, writing papers in this way has worked pretty well for us so far. We hope that this guide can at least partially do the same for others.
Danielle S. Willkens, Junshan Liu, Shadi Alathamneh
The Edmund Pettus Bridge, Selma, Alabama, a symbol of the American Civil Rights Movement and an exemplar of early 20th-century engineering, stands as a testament to the progress and challenges of its era. The bridge, recognized for its pivotal role in the 1965 “Bloody Sunday” conflict and the following Selma to Montgomery marches for voting rights, also represents significant engineering achievements with its distinctive design and construction methodology. In this study, the research team presents a comprehensive framework for documenting heritage bridges by utilizing Terrestrial Laser Scanning (TLS) technology, supplemented by other Reality Capture (RC) techniques, including Structure from Motion (SfM), 360-degree photography, and Unmanned Aerial Vehicle (UAV), and integrating the data within a Building Information Modeling (BIM) environment. The focus on the Edmund Pettus Bridge case study demonstrates how this novel approach can capture the intricate details of its structural and architectural features while preserving its historical narratives. The documentation outcomes, including a detailed BIM model and a set of Historic American Engineering Record (HAER) drawings, highlight the effectiveness of combining TLS and BIM in conserving unconventional heritage structures like bridges. This paper also discusses the technological challenges encountered, such as dealing with heavy traffic and environmental constraints during data acquisition and developing the BIM model and drawings. It outlines the strategies implemented to address these issues. This research contributes to preserving a severely under-represented American National Historic Landmark (NHL). It sets a precedent for documenting other non-building heritage structures, balancing technological advancements with historical integrity.
Design for manufacture and assembly (DfMA) in the architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current need of further research and development. The DfMA’s conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concepts and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures for Dfab, DfAM, and AM assembly processes were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. As Dfab and DFAM are innovated, practical DFMA techniques begin to develop to a great extent. Large research gaps in the DfMA for Dfab and DfAM can be filled in terms of integrating them with product structural performance, management, studied cases, building information modeling (BIM), and machine learning to increase operational efficiency and sustainable practices.
As buildings and structures age, the challenges of reinforcement and retrofitting become more significant, especially as their service life extends and the demand for seismic fortification increases. Integrating buckling‐restrained braces (BRBs) is an effective retrofit technique; however, this approach requires multiple iterations of layout adjustments and mechanical performance analysis, which are highly dependent on engineers' design expertise, resulting in low efficiency. To address this, the study proposes a two‐stage intelligent retrofit design method that integrates generative Artificial intelligence (AI) techniques with optimization algorithms for reinforced concrete (RC) frame structures using BRBs: (1) a diffusion model‐based potential BRB layout generation stage, and (2) an online learning algorithm‐based design optimization stage. In Stage 1, a diffusion model was employed to analyze architectural characteristics, identify potential BRB locations, narrow the feasible solution space for the optimization process, and ensure that the design meets empirical constraints. In Stage 2, an optimization algorithm, integrated with mechanical performance evaluation, was employed to determine the optimal locations and sizes of BRBs. Case studies revealed that these two methods enhanced efficiency by approximately 50 times compared to the direct design by engineers while maintaining design rationality and safety. Overall, these results demonstrate the feasibility and generalizability of the method in practical engineering applications, offering a reference for the intelligent design of more complex structural retrofits in the future.
Reducing concrete consumption is important as part of the global effort of fighting the climate change, and specifically in concrete flat slabs as these are among the largest cement consumers. In this study we formulate an efficient gradient-based optimization of column locations, that minimizes the slabs’ thickness with constraints on the deflections, bending moments and shear stresses while accounting for architectural considerations. The results show that the columns’ optimal locations are not trivial and that the slab thickness is very sensitive to the columns’ exact locations. Thus, concrete savings in slabs of up to 20% are possible with minor modification to traditional layouts of columns, and up to 50% with more pronounced updates, which emphasizes the importance of early collaboration between architects and engineers. The results indicate the critical trade-off between structural efficiency and architectural freedom and demonstrate the potential of formal optimization in structural design.
Recently, the employment of topology optimization (TO) in structural engineering design has gained a significant structural performance, also, TO is employed by designers for developing aesthetically and efficient buildings. In this work, TO is employed to design novel and rigged structures of communication towers. The way that the TO algorithm works is to intelligently create the 3D model by preserving architectural features with tradeoffs between the stiffness and weight ratio. The present work focuses on the investigation of creating optimal self-supported communication towers. Results concerning the prime observation of optimization analyses and the potential benefits of TO in designing telecommunication tower lattices are drawn.
In recent years, fiber-reinforced polymers (FRP) have emerged as a highly effective solution for strengthening reinforced concrete (RC) structures. However, accurately assessing the fire resistance of FRP-strengthened members remains a significant challenge due to the limited guidance available in current building codes, often leading to conservative and cost-intensive evaluations. Experimental testing and numerical analysis required for such assessments are resource-demanding, highlighting the need for more efficient methods. This study investigates the application of machine learning (ML) techniques to predict the fire resistance of FRP-strengthened RC beams. Twelve ML models, including eight ensemble methods and four traditional approaches, were employed. The models were trained using a comprehensive dataset comprising over 21,000 data points obtained from numerical simulations and experimental tests. The dataset captured variations in geometric configurations, insulation strategies, loading conditions, and material properties. To enhance predictive accuracy, Bayesian optimization and k-fold cross-validation were applied for model tuning, while the Shapley Additive Explanations (SHAP) method was utilized to assess the relative importance of features influencing fire resistance. Among the models tested, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), and Gradient Boosting (GRB) demonstrated superior performance, achieving accuracy rates exceeding 92%. Key factors identified as significantly affecting fire resistance included loading ratio, area of tensile reinforcement, insulation depth, concrete cover thickness, and FRP area. The findings underscore the potential of ensemble ML techniques over traditional methods for accurately predicting the fire resistance of FRP-strengthened RC beams, offering critical insights for optimizing design practices and enhancing structural fire safety.
Architectural engineering. Structural engineering of buildings, Structural engineering (General)
In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM's propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal.
Sonja Hyrynsalmi, Ella Peltonen, Fanny Vainionpää
et al.
In the extant literature, there has been discussion on the drivers and motivations of minorities to enter the software industry. For example, universities have invested in more diverse imagery for years to attract a more diverse pool of students. However, in our research, we consider whether we understand why students choose their current major and how they did in the beginning decided to apply to study software engineering. We were also interested in learning if there could be some signs that would help us in marketing to get more women into tech. We approached the topic via an online survey (N = 78) sent to the university students of software engineering in Finland. Our results show that, on average, women apply later to software engineering studies than men, with statistically significant differences between genders. Additionally, we found that marketing actions have different impacts based on gender: personal guidance in live events or platforms is most influential for women, whereas teachers and social media have a more significant impact on men. The results also indicate two main paths into the field: the traditional linear educational pathway and the adult career change pathway, each significantly varying by gender
Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as well as green LLM4SE solutions that reduce energy consumption, water usage, and carbon emissions. This paper aims to redirect the focus of the research community towards the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance of LLM4SE and highlights the need for efficient and green LLM4SE solutions. Subsequently, the paper presents a vision for a future where efficient and green LLM4SE revolutionizes the LLM-based software engineering tool landscape, benefiting various stakeholders, including industry, individual practitioners, and society. The paper then delineates a roadmap for future research, outlining specific research paths and potential solutions for the research community to pursue. While not intended to be a definitive guide, the paper aims to inspire further progress, with the ultimate goal of establishing efficient and green LLM4SE as a central element in the future of software engineering.
Ethnography has become one of the established methods for empirical research on software engineering. Although there is a wide variety of introductory books available, there has been no material targeting software engineering students particularly, until now. In this chapter we provide an introduction to teaching and learning ethnography for faculty teaching ethnography to software engineering graduate students and for the students themselves of such courses. The contents of the chapter focuses on what we think is the core basic knowledge for newbies to ethnography as a research method. We complement the text with proposals for exercises, tips for teaching, and pitfalls that we and our students have experienced. The chapter is designed to support part of a course on empirical software engineering and provides pointers and literature for further reading.
Maria Soler Sala, Roberta Ferretti, Federico Cioli
This research is part of the European project F-ATLAS – Franciscan Landscapes the Observance between Italy, Portugal and Spain, which aim is to study the Franciscan Observance network and to find effective strategies for the conservation, protection and promotion of this important heritage. The contribution is focused on the multidisciplinary study of the Monastery of Sant Miquel d’Escornalbou (Tarragona, Spain). The historical, architectural and patrimonial research on this last and interesting centre of medieval spirituality has been developed jointly by the Italian and Spanish teams of the project. From its foundation until the last reconstruction by Eduard Toda around 1910, the complex’s function and shape have changed significantly: during the centuries, several interventions have modified it to the point of making it difficult today to read its origin and evolutionary phases. The integrated laser-scanner and photogrammetric survey, together with the creation of a digital catalogue of geo-referenced convents and the results of the international workshop carried out in November 2021, represent the bases for further analysis regarding the evolutionary phases of the complex, the buildings’ structure conditions and the definition of possible strategies for redevelopment.
Environmental technology. Sanitary engineering, Architectural engineering. Structural engineering of buildings
This paper describes how motivational models can be used to cross check agile requirements artifacts to improve consistency and completeness of software requirements. Motivational models provide a high level understanding of the purposes of a software system. They complement personas and user stories which focus more on user needs rather than on system features. We present an exploratory case study sought to understand how software engineering students could use motivational models to create better requirements artifacts so they are understandable to non-technical users, easily understood by developers, and are consistent with each other. Nine consistency principles were created as an outcome of our study and are now successfully adopted by software engineering students at the University of Melbourne to ensure consistency between motivational models, personas, and user stories in requirements engineering.