期刊信息

Information and Software Technology (IST)

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影响因子:
4.3
出版商:
Elsevier
ISSN:
0950-5849
浏览:
40801
关注:
26

征稿

Information and Software Technology (IST) is an academic journal published by Elsevier. (ISSN 0950-5849, impact factor 4.3, CCF B).

Visit the journal's proposal guidelines to submit a proposal for a special issue (original contributions on a topic within the scope of the journal) or a special section with extended papers from a conference of workshop within the scope of the journal. Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal's scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include: • Software management, quality and metrics, • Software processes, • Software architecture, modelling, specification, design and programming • Functional and non-functional software requirements • Software testing and verification & validation • Empirical studies of all aspects of engineering and managing software development Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information. The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering. Guidelines for conducting systematic reviews are provided here.
由 Dou Sun 最后更新于

Special Issues

Special Issue on Human Factor in Generative AI: Trust, Usability, and Responsible Adoption 截稿日期: 2026-07-31 Generative AI (GenAI) tools such as ChatGPT, Copilot, and MidJourney are transforming the way we design, code, and collaborate. Yet, the human factor—trust, usability, ethical decisionmaking, and responsible adoption—remains a critical challenge. This Special Issue invites original contributions that explore empirical evidence, human-centric frameworks, and practical case studies on the intersection of humans and GenAI. Guest editors: Dr. Abdul Rehman Gilal, (Lead Guest Editor)Florida International University, USA Dr. Rehan Akbar, Florida International University, USA Dr. Mazni Omar, School of Computing, Universiti Utara Malaysia, Malaysia Special issue information: We welcome submissions from software engineering, human–computer interaction, cognitive science, psychology, ethics, and interdisciplinary fields that advance understanding of how humans interact with, rely on, and govern GenAI. Topics of Interest (include but not limited to): Human trust, transparency, and explainability in GenAI systems Cognitive and behavioral impacts of GenAI on developers, educators, and end-users Human-in-the-loop approaches and responsible GenAI adoption Ethical, legal, and governance challenges in GenAI usage Over-reliance, misuse, and risk perception in GenAI adoption Empirical studies of human interaction with GenAI in software engineering, education,and creative domains Frameworks and tools for training, awareness, and governance of GenAI Case studies on organizational adoption of GenAI and workforce transformation https://www.sciencedirect.com/special-issue/329824/human-factor-in-generative-ai-trust-usability-and-responsible-adoption
由 Dou Sun 最后更新于
Special Issue on Green Software Evolution 截稿日期: 2026-08-28 The Greenvolve special issue aims to bring together researchers and practitioners from software engineering, artificial intelligence, quantum computing, and sustainability to explore how green practices can be integrated into software design and evolution, address the challenges of embedding sustainability metrics into modern development workflows, foster interdisciplinary collaboration, and identify future directions and research opportunities for reducing the environmental impact of digital infrastructure. This special issue features an open call for submissions presenting original, unpublished results that focus on integrating sustainability into software engineering practices. It also welcomes extended versions of papers accepted at the Green Software Evolution (Greenvolve) workshop at the International Conference on Software Analysis, Evolution, and Reengineering (SANER 2026). Such submissions must include substantial additional contributions beyond the workshop version, such as new methodologies, case studies, or tools. Guest editors: Dr. Fernando Castor, University of Twente, The Netherlands Dr. Emanuela Guglielmi, University of Molise, Italy Dr. Luana Martins, University of Salerno, Italy Dr. Michele Tucci, SPENCER Lab, University of L’Aquila, Italy Special issue information: Topics of interest include, but are not limited to: • Green software design, architecture, maintenance, and evolution • Green performance testing and regression detection • Metrics and modeling for green software • Automation and tooling for green software • Benchmarks for performance and sustainability • Green DevOps practices • Empirical studies, case studies, experience reports, and practices on green software • Green AI and machine learning • Quantum computing and sustainability • Carbon footprint assessment and optimization in software Manuscript submission information: All submissions must follow the Information and Software Technology (IST) journal guidelines at Guide for Authors - Information and Software Technology Manuscripts should be submitted online via Elsevier’s Editorial Manager system Submission site for Information and Software Technology and will undergo rigorous peer review according to IST standards. Authors should select “VSI: Greenvolve” for the “Article Type” when submitting. Important dates: Paper submission opening: 30 Jan, 2026 Paper submission deadline: 28 Aug, 2026 Keywords: sustainability; Green Software; Artificial Intelligence; quantum computing; DevOps
由 Dou Sun 最后更新于

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