Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Eduard Enoiu, Jean Malm, Gregory Gay

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.

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.

Martin Obaidi, Marc Herrmann, Elisa Schmid et al.

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.

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.

Filipe R. Cogo, Gustavo A. Oliva, Ahmed E. Hassan

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.

Roberto Verdecchia, Justus Bogner

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.

Jilong Zhao, Qing Wang, Qingfeng Han

In this paper, a novel axial flux reversal permanent magnet (PM) (AFRPM) machine, which combines the merit of the axial flux PM (AFPM) machine and flux reversal PM machine, is proposed. Meanwhile, according to the characteristics of the machine, a method to reduce the torque ripple is presented and researched. Firstly, the topology and operation principle of the machine are analyzed. The power-size equation is derived and the design scheme is confirmed to obtain the structure parameters of the machine. Then, the general electromagnetic performances are analyzed. Secondly, the magnetomotive force (MMF)-permeance model is established. The air gap flux density harmonic distribution principle is researched from the air gap field modulation perspective. The torque generation mechanism is studied by analyzing the influences of the armature reaction air gap flux density harmonics and no-load air gap flux density harmonics on the electromagnetic torque. Furthermore, in order to reduce the torque ripple, an optimization method using the cos-type PMs, which suppress the harmonic pole pairs that do not contribute to the torque output, is investigated. To simplify the manufacturing process, the segmented sector PMs with different heights and arcs are employed instead of the cos-type PM. The effects of the different numbers of segmented sector PMs on the torque ripple are studied. Finally, the results indicate that the proposed machine exhibits large torque capability and high power/torque density. Meanwhile, the torque ripple is significantly reduced by the optimization method.

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

Jieke Shi, Zhou Yang, David Lo

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.

Yvonne Dittrich, Helen Sharp, Cleidson de Souza

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.

Juan M. Murillo, Jose Garcia-Alonso, Enrique Moguel et al.

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective -key qualities of any industry-grade software-mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyse in depth the current state of quantum software engineering research. From this analysis, the key areas of quantum software engineering are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing Quantum Software Engineering.

Ranim Khojah, Mazen Mohamad, Philipp Leitner et al.

Large Language Models (LLMs) are frequently discussed in academia and the general public as support tools for virtually any use case that relies on the production of text, including software engineering. Currently there is much debate, but little empirical evidence, regarding the practical usefulness of LLM-based tools such as ChatGPT for engineers in industry. We conduct an observational study of 24 professional software engineers who have been using ChatGPT over a period of one week in their jobs, and qualitatively analyse their dialogues with the chatbot as well as their overall experience (as captured by an exit survey). We find that, rather than expecting ChatGPT to generate ready-to-use software artifacts (e.g., code), practitioners more often use ChatGPT to receive guidance on how to solve their tasks or learn about a topic in more abstract terms. We also propose a theoretical framework for how (i) purpose of the interaction, (ii) internal factors (e.g., the user's personality), and (iii) external factors (e.g., company policy) together shape the experience (in terms of perceived usefulness and trust). We envision that our framework can be used by future research to further the academic discussion on LLM usage by software engineering practitioners, and to serve as a reference point for the design of future empirical LLM research in this domain.

Shae T. Hart, Jake Kamenetsky, Christopher A. Kitts

Solving the robotic swarm coverage problem for an elliptical area has various applications for exploring novel environments. Solutions for this problem should cover a specified ellipse and seamlessly adapt to changing numbers of robots. Previous solutions used techniques such as formation control, vector fields, and neural networks. While these techniques were successful, they all lacked one or more of the three key tenants of swarm elliptical attraction: complete coverage of an ellipse with commandable parameters, simplicity for scaling in the number of robots, and adaptive sizing. Additionally, no previous work presented guidelines for ensuring that the swarms could successfully and safely converge to the commanded ellipse without collisions. In contrast, this work presents a novel swarm elliptical attraction behavior with all three key tenants with guidelines for ellipse and swarm parameter selection. First, a new Lyapunov stable elliptical attraction behavior for Reactive Particle Swarms is presented. The behavior commands robots to cover the entire ellipse area for a specific semimajor axis, eccentricity, and orientation. Additionally, dynamic interagent spacing naturally ensures coverage for different numbers of robots. Second, the work presents a novel adaptive sizing algorithm that varies the ellipse’s semimajor axis based on the swarm state. The adaptive sizing algorithm specifies the eccentricity and orientation using time-varying functions. Third, guidelines for selecting the number of robots, commanded ellipse area, obstacle avoidance distance, and robot communication range that allow for successful aggregation to the commanded ellipse are presented. All three of the results are verified using simulation and hardware-in-the-loop trials.

Ting Zhang, Ivana Clairine Irsan, Ferdian Thung et al.

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.

Lvyang Yang, Jiankang Zhang, Huaiqiang Li et al.

The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.

B. Cao, L. Wang, Y.W. Yu et al.

Particle control is one of the key issues for steady-state tokamak operation. The density decay time is used to evaluate the capability of the divertor to exhaust particle with all fueling turn off. Experiments of the EAST divertor's ability to exhaust helium particles were also performed during the EAST helium plasma operation. The experimental results show that the EAST divertor is less capable of exhaust helium particles than deuterium particles. And similar to deuterium, the helium particle exhaust performance is sensitivity on the different strike point location. During 2021 experiment campaign, a new lower tungsten divertor has been developed and installed on EAST, the particle exhaust capacity of the new divertor has also been investigated during this campaign.

H. K. Lee, Y.‐S. Choi

Abstract Recent advancements in steady‐state visual evoked potential‐based brain‐computer interface have been made possible by the widespread use of various spatial filtering methods. Task‐related component analysis has superiority over existing subject‐specific target frequency recognition methods. However, the optimised spatial filters of task‐related component analysis generated from a small training dataset are susceptible to artefacts and noise, which can be overfitted, particularly in short time windows. To tackle this issue, the authors propose a regularised task‐related component analysis that adopts three regularisation approaches to the objective function of task‐related component analysis. Conventionally, the regularisation method is a simple and efficient way to overcome the overfitting problem, especially for a small training dataset. To this end, the proposed regularised task‐related component analyses outperform the conventional task‐related component analysis in terms of average classification accuracy and information transfer rate.

Zhichao Tang, Jin Cui, Muzhi Yu et al.

The diffusion growth of intermetallic compounds in Al-Er alloys are closely related to the properties of the alloys. The current work aims at explaining the dominance of Al3Er in the Al-Er alloys precipitation phases and the interface thin layer phenomenon by diffusion couple technique, estimating the parabolic growth constant and diffusion activation energy of intermetallic compound in Al-Er diffusion couples to provide theoretical guidance for the design of new Al-Er alloys. In this work, Al-Er diffusion couples were successfully prepared by casting-cladding method in the atmosphere. The growth of Al-Er intermetallic compounds at diffusion couple interface during annealing were observed and recorded by High-Temperature Laser-Scanning Confocal Microscopy at 673, 698, 723 and 748 K respectively. The results show that the growth characteristics of Al-Er intermetallic compounds were accord with layer-terraced growth during annealing. The thickness of intermetallic compound was linear with the square root of time at experimental temperature. The intermetallic compound layer was composed of Al3Er and a very thin AlEr phase. The parabolic growth constants of Al3Er phase at 673, 698, 723 and 748 K were 1.017 × 10−14, 1.609 × 10−14, 3.111 × 10−14 and 4.76 × 10−14 respectively. The activation energy of Al3Er phase was (88.4 ± 5.3) kJ/mol and the pre-exponential factor was 7.126 × 10−8 m2/s.

Dulaji Hidellaarachchi, John Grundy, Rashina Hoda et al.

Context: Requirements engineering (RE) is an important part of Software Engineering (SE), consisting of various human-centric activities that require the frequent collaboration of a variety of roles. Prior research has shown that personality is one such human aspect that has a huge impact on the success of a software project. However, a limited number of empirical studies exist focusing on the impact of personality on RE activities. Objective: The objective of this study is to explore and identify the impact of personality on RE activities, provide a better understanding of these impacts, and provide guidance on how to better handle these impacts in RE. Method: We used a mixed-methods approach, including a personality test-based survey (50 participants) and an in-depth interview study (15 participants) with software practitioners from around the world involved in RE activities. Results: Through personality test analysis, we found a majority of the practitioners have a high score on agreeableness and conscientiousness traits and an average score on extraversion and neuroticism traits. Through analysis of the interviews, we found a range of impacts related to the personality traits of software practitioners, their team members, and external stakeholders. These impacts can be positive or negative, depending on the RE activities, the overall software development process, and the people involved in these activities. Moreover, we found a set of strategies that can be applied to mitigate the negative impact of personality on RE activities. Conclusion: Our identified impacts of personality on RE activities and mitigation strategies serve to provide guidance to software practitioners on handling such possible personality impacts on RE activities and for researchers to investigate these impacts in greater depth in future.

M. Costea, I. Băran, T. Leonida