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IEEE Software

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インパクトファクター:
3.3
出版社:
IEEE
ISSN:
0740-7459
閲覧:
27767
フォロー:
10

論文募集

IEEE Software is an academic journal published by IEEE. (ISSN 0740-7459, impact factor 3.3).

IEEE Software’s mission is to be the best source of reliable, useful, peer-reviewed information for leading software practitioners—the developers and managers who want to keep up with rapid technology change. The authority on translating software theory into practice, this bimonthly magazine positions itself between pure research and pure practice, transferring ideas, methods, and experiences among researchers and engineers. Peer-reviewed articles and columns by real-world experts illuminate all aspects of the industry, including process improvement, project management, development tools, software maintenance, web applications and opportunities, testing, and usability. Technical articles are peer-reviewed carefully to ensure they offer practical and reliable ideas and techniques to readers. The departments cover key concerns of software development: requirements, design, architecture, tools, technologies, empirical evidence, and quality. We also publish insightful lessons-learned articles by people with stories to tell. Software engineering experts in architecture and design, quality, project management, education, requirements, and many other areas of expertise help guide the selection of what we publish. Our Letters to the Editor, Point-Counterpoint debates, and Sounding Board contributions from thought leaders challenge the status quo and stimulate debate. The magazine has been helping train newcomers to software engineering since 1984, as professors, project managers, and developers pass on articles for their colleagues and students to read.
最終更新：Dou Sun（）

Special Issues

Special Issue on The Edge–Cloud Continuum: Software Challenges and Innovations 投稿締切日: 2026-07-07 The software-powered world increasingly spans from constrained devices and on‑premise micro‑datacenters to regional edge sites and public clouds. Building and operating software across this edge‑to‑cloud continuum poses unique challenges such as intermittent connectivity, diverse hardware accelerators, strict latency requirements, data residency constraints, Operational Complexity and many others. Cloud‑native foundations (containers, serverless, service meshes) are being reimagined for the edge; AI/ML pipelines are moving closer to where data is produced; and advances in 5G/6G and time‑sensitive networking are expanding what’s possible. This special issue aims to surface cutting‑edge approaches, architectures, and practices that enable dependable, secure, and sustainable software spanning edge and cloud. We welcome contributions that clarify how engineering teams design, deliver, observe, and govern software across the continuum; report on impact to quality, reliability, cost, performance, and sustainability; and share lessons learned from real‑world deployments in industry and the public sector. We invite researchers, practitioners, and industry experts to submit original contributions to the IEEE Software Special Issue on Edge‑to‑Cloud Continuum and Infrastructures. Topics of interest include, but are not limited to: Reference architectures & patterns for edge–cloud applications (event‑driven, digital twins, streaming, offline‑first). CI/CD and progressive delivery across the continuum (OTA updates, canary/blue‑green at the edge, safe rollback, compliance gates). Orchestration & resource management across constrained/heterogeneous nodes (containers, micro‑VMs/unikernels, multi‑cluster, accelerator‑aware scheduling). Serverless and service meshes spanning WAN and intermittency; function fabrics from device to cloud. Data pipelines & management (state synchronization, consistency models, conflict resolution, data residency/sovereignty). Observability, SRE & AIOps for edge–cloud systems (cross‑layer telemetry, anomaly detection, incident response at the edge). MLOps on the continuum (on‑device inference, model rollout/drift, federated learning, privacy‑preserving training). Security, privacy & trust (zero trust for edge, confidential computing/attestation, secure boot, SBOM and supply‑chain risk). Reliability & resilience (fault tolerance, self‑healing, network partitions, real‑time constraints, chaos engineering for edge). Network‑aware engineering (5G/6G, MEC, slicing, TSN, satellite‑edge integration) and their implications for software design. Testing, verification & benchmarking for distributed edge–cloud software; representative testbeds and reproducibility. Sustainability & cost effectiveness (energy‑/carbon‑aware placement, FinOps for edge, lifecycle impacts). Developer experience & platform engineering (SDKs/abstractions, APIs, policy‑as‑code, governance and compliance). Domain case studies and experience reports (smart railway, smart transportation, industrial IoT/Industry 4.0, automotive, healthcare, retail, smart cities, public safety). Intent-based management and orchestration (policy translation, closed-loop control, intent verification, conflict resolution, self-optimizing orchestration). https://www.computer.org/digital-library/magazines/so/cfp-edge-cloud-continuum
最終更新：Dou Sun（）
Special Issue on Human-Centric AI for Software Engineering 投稿締切日: 2026-09-07 Artificial Intelligence (AI) is reshaping nearly every facet of software systems — from how we design, test, and deploy them, to how they are maintained and evolved. While AI-driven capabilities promise to dramatically improve software quality and developer productivity, they also raise fundamental challenges: explainability, trust, fairness, human oversight, control, and accountability. These challenges are inherently human-centric: software engineers, users, and stakeholders interact with AI systems, make decisions based on them, and must understand their behavior. In the context of software engineering, a human-centric perspective on AI is essential. When we embed AI components inside software development pipelines or products, we are not merely building smart code — we are co-creating socio-technical systems. The human actors (developers, testers, operators, end users) shape, adapt to, and are affected by these systems. Ignoring human aspects can lead to misaligned goals, a lack of user trust, unintended behaviors, or systemic failures. This special issue of IEEE Software invites high-quality contributions exploring the intersection of AI, software engineering, and human-centric design. We welcome papers from two complementary angles: (1) Human-centered AI, focusing on how humans understand, collaborate with, and shape AI within software engineering workflows; and (2) AI for humans, highlighting how AI can support, augment, and empower developers, testers, operators, and end users. Submissions should emphasize how software engineering methods, tools, and processes can be designed or adapted to preserve human agency, understanding, oversight, and participation when building or using AI-enabled systems —keeping human needs and roles central, rather than addressing trustworthiness or technical properties in isolation. Contributions addressing both developer-focused human-AI topics and end-user-focused interaction and co-creation are encouraged. We seek practical, experience-based, and research-grounded articles that address the integration of human-centric principles into AI for software engineering. Submissions should offer value to practitioners while being grounded in evidence, case studies, frameworks, tools, or reflections on real-world practice. Topics of interest include, but are not limited to: Explainable, interpretable, and transparent AI for developers, operators, and end users Human-in-the-loop and human-AI collaborative development tools Governance, safety, trust, accountability, and ethical alignment in AI-augmented systems Human factors, mental models, and developer experience with AI tools Fairness, bias mitigation, and inclusive AI system design AI-assisted requirements, design, testing, maintenance, and evolution Participatory design, co-creation, and multi-stakeholder collaboration in AI-enabled software Socio-technical ecosystems and organizational practices for human-centered AI DevOps/MLOps pipelines that embed human oversight and feedback End-User interaction with AI-augmented software features Human-AI co-creation for end-user development Empirical and longitudinal studies of AI in software engineering practice Education and workforce development for human-centric AI engineering https://www.computer.org/digital-library/magazines/so/cfp-human-centric-ai
最終更新：Dou Sun（）

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CCF正式名称インパクトファクター出版社ISSN
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