Hasil untuk "Engineering"

C. Aakeröy, K. Seddon

R. Singh, D. Heldman

J. Blanshard

E. Lewis

T. Chandrupatla, A. Belegundu

Thoroughly updated with improved pedagogy, the fifth edition of this classic textbook continues to provide students with a clear and comprehensive introduction the fundamentals of the finite element method. New features include coverage of core topics – including mechanics and heat conduction, energy and Galerkin approaches, convergence and adaptivity, time-dependent problems, and computer implementation – in the context of simple 1D problems, before advancing to 2D and 3D problems; expanded coverage of reduction of bandwidth, profile and fill-in for sparse solutions, time-dependent problems, plate bending, and nonlinearity; over thirty additional solved problems; and downloadable Matlab, Python, C, Javascript, Fortran and Excel VBA code providing students with hands-on experience. Accompanied by online solutions for instructors, this is the definitive text for senior undergraduate and graduate students studying a first course in the finite element method, and for professional engineers keen to shore up their understanding of finite element fundamentals.

W. Smith

R. Flagan, J. Seinfeld

D.V.Razevig

C. U. Smith

P. Doran

Tatsuya Shimizu, M. Yamato, A. Kikuchi et al.

D. Butler, S. Goldstein, F. Guilak

Sofia Tapias Montana, Ronnie de Souza Santos

This paper investigates how software professionals perceive the economic implications of diversity in software engineering teams. Motivated by a gap in software engineering research, which has largely emphasized socio-technical and process-related outcomes, we adopted a qualitative interview approach to capture practitioners' reasoning about diversity in relation to economic and market-oriented considerations. Based on interviews with ten software professionals, our analysis indicates that diversity is perceived as economically relevant through its associations with cost reduction and containment, revenue generation, time to market, process efficiency, innovation, and market alignment. Participants typically grounded these perceptions in concrete project experiences rather than abstract economic reasoning, framing diversity as a practical resource that supports project delivery, competitiveness, and organizational viability. Our findings provide preliminary empirical insights into how economic aspects of diversity are understood in software engineering practice.

Mamdouh Alenezi

The rapid proliferation of large language models (LLMs) and agentic AI systems has created an unprecedented abundance of automatically generated code, challenging the traditional software engineering paradigm centered on manual authorship. This paper examines whether the discipline should be reoriented around orchestration, verification, and human-AI collaboration, and what implications this shift holds for education, tools, processes, and professional practice. Drawing on a structured synthesis of relevant literature and emerging industry perspectives, we analyze four key dimensions: the evolving role of the engineer in agentic workflows, verification as a critical quality bottleneck, observed impacts on productivity and maintainability, and broader implications for the discipline. Our analysis indicates that code is transitioning from a scarce, carefully crafted artifact to an abundant and increasingly disposable commodity. As a result, software engineering must reorganize around three core competencies: effective orchestration of multi-agent systems, rigorous verification of AI-generated outputs, and structured human-AI collaboration. We propose a conceptual framework outlining the transformations required across curricula, development tooling, lifecycle processes, and governance models. Rather than diminishing the role of engineers, this shift elevates their responsibilities toward system-level design, semantic validation, and accountable oversight. The paper concludes by highlighting key research challenges, including verification-first lifecycles, prompt traceability, and the long-term evolution of the engineering workforce.

Recep Evrim Ozgen, Ahmet Yazar, Mustafa Serdar Osmanca

Future sixth-generation (6G) wireless networks are expected to evolve beyond traditional communication infrastructures and incorporate native sensing and environmental awareness capabilities. While previous generations primarily focused on connectivity metrics such as data rate, latency, and radio-frequency (RF) coverage, emerging applications increasingly require networks that can perceive physical environments and support context-aware decision making. This paper introduces the concept of sensing coverage as a complementary performance dimension to conventional RF coverage in sensing-enabled wireless systems. Within this perspective, a unified 6G vision is presented in which wireless infrastructure operates as a distributed sensing platform enabled by integrated sensing and communication (ISAC). The complementary paradigms of Network for Sensing, Sensing for Network, and Sensing-as-a-Service are systematically analyzed to clarify their roles in sensing-centric wireless architectures. The paper further reviews heterogeneous sensing technologies including cellular sensing, Wi-Fi sensing, visible-light communications (VLC), non-terrestrial networks (NTN), terahertz (THz) communications, and reconfigurable intelligent surface (RIS)-assisted sensing, illustrating how these technologies jointly enable multi-layer sensing coverage. In addition, quantitative foundations and performance metrics for sensing coverage are presented, and an illustrative evaluation is provided to highlight the fundamental differences between RF communication coverage and sensing coverage. Finally, the roles of AI-native intelligence, digital twins, and sensing-oriented service models are discussed in the context of sensing-enabled 6G networks. The presented framework provides a structured view of the emerging sensing-centric 6G ecosystem and highlights key research directions for future wireless systems that jointly integrate communication, sensing, computation, and intelligent services.

M. Csete, J. Doyle

Guoping Chen, T. Ushida, T. Tateishi

Alex Gu, Naman Jain, Wen-Ding Li et al.

AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.

Christoph Treude, Margaret-Anne Storey

The adoption of large language models (LLMs) and autonomous agents in software engineering marks an enduring paradigm shift. These systems create new opportunities for tool design, workflow orchestration, and empirical observation, while fundamentally reshaping the roles of developers and the artifacts they produce. Although traditional empirical methods remain central to software engineering research, the rapid evolution of AI introduces new data modalities, alters causal assumptions, and challenges foundational constructs such as "developer", "artifact", and "interaction". As humans and AI agents increasingly co-create, the boundaries between social and technical actors blur, and the reproducibility of findings becomes contingent on model updates and prompt contexts. This vision paper examines how the integration of LLMs into software engineering disrupts established research paradigms. We discuss how it transforms the phenomena we study, the methods and theories we rely on, the data we analyze, and the threats to validity that arise in dynamic AI-mediated environments. Our aim is to help the empirical software engineering community adapt its questions, instruments, and validation standards to a future in which AI systems are not merely tools, but active collaborators shaping software engineering and its study.

Joshua Owotogbe, Indika Kumara, Dario Di Nucci et al.

Chaos engineering aims to improve the resilience of software systems by intentionally injecting faults to identify and address system weaknesses that cause outages in production environments. Although many tools for chaos engineering exist, their practical adoption is not yet explored. This study examines 971 GitHub repositories that incorporate 10 popular chaos engineering tools to identify patterns and trends in their use. The analysis reveals that Toxiproxy and Chaos Mesh are the most frequently used, showing consistent growth since 2016 and reflecting increasing adoption in cloud-native development. The release of new chaos engineering tools peaked in 2018, followed by a shift toward refinement and integration, with Chaos Mesh and LitmusChaos leading in ongoing development activity. Software development is the most frequent application (58.0%), followed by unclassified purposes (16.2%), teaching (10.3%), learning (9.9%), and research (5.7%). Development-focused repositories tend to have higher activity, particularly for Toxiproxy and Chaos Mesh, highlighting their industrial relevance. Fault injection scenarios mainly address network disruptions (40.9%) and instance termination (32.7%), while application-level faults remain underrepresented (3.0%), highlighting for future exploration.