Academic Journal
Q1ACM Computing Surveys
About ACM Computing Surveys
ACM Computing Surveys is a scholarly journal published by Association for Computing Machinery. SCImago 2025 places it in Q1 with an SJR of 5.985 and an H-index of 260.
Its listed coverage is 1969-2025 and its research categories include Computer Science (miscellaneous) (Q1); Theoretical Computer Science (Q1). The 2025 dataset reports 383 documents and 39294 citations across the latest three-year reporting window.
ACM Computing Surveys (CSUR) is a top-tier, peer-reviewed journal published by the Association for Computing Machinery (ACM). It stands out in the world of computer science for its focus on comprehensive survey articles that review and synthesize large areas of computing research. With a strong academic foundation and wide industry relevance, CSUR is recognized globally as a trusted source of knowledge across both foundational and emerging areas of computing.
What Is ACM Computing Surveys?
Unlike traditional journals that publish original research studies, ACM Computing Surveys focuses exclusively on survey articles. These articles summarize, analyze, and evaluate the current state of a particular research area. Each survey is written by domain experts and provides a structured overview of hundreds of published works, offering readers a deep understanding of a topic's evolution, current challenges, and future directions.
Survey topics span a wide range of disciplines, including:
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Artificial Intelligence and Machine Learning
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Human-Computer Interaction (HCI)
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Cybersecurity and Privacy
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Data Science and Big Data Analytics
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Cloud and Edge Computing
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Blockchain and Distributed Systems
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Internet of Things (IoT)
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Quantum Computing
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Theoretical Computer Science
Why CSUR Matters in the Digital Age
In today’s fast-paced tech environment, staying current with emerging technologies is essential. ACM Computing Surveys serves as a knowledge bridge—helping researchers, educators, and professionals understand complex technologies and anticipate future trends. Its highly cited articles often become foundational texts in academic courses and research programs.
From graduate students to seasoned engineers, CSUR offers valuable insights that guide research, product development, and decision-making in both academia and industry.
SEO and Digital Innovation Relevance
For professionals in search engine optimization (SEO), AI, and digital strategy, CSUR provides a solid academic grounding in the technologies shaping modern digital platforms. Articles on natural language processing (NLP), machine learning models, semantic web, and information retrieval systems offer critical insights into how search engines operate and evolve.
Staying updated with survey articles from CSUR can significantly benefit content strategists, data analysts, and developers who need a deeper understanding of the tools and algorithms driving search performance, content ranking, and user behavior analysis.
A Trusted Resource with Global Impact
ACM Computing Surveys is indexed in major scientific databases and boasts a high impact factor, making it one of the most influential publications in computer science. It is widely read by university faculty, students, industry researchers, and technology leaders around the world.
With its rigorous peer-review process and editorial standards, the journal ensures that each article is authoritative, up-to-date, and highly relevant.
Journal Metrics
Metrics can change by reporting year. Verify time-sensitive values with the publisher or indexing service.
Aims & Scope
ACM Computing Surveys (CSUR) is one of the most prestigious and widely respected journals in the field of computer science. Published by the Association for Computing Machinery (ACM), CSUR is known for its unique focus on publishing high-quality survey articles that thoroughly review the current state of research across all areas of computing. The scope of ACM Computing Surveys is broad, multidisciplinary, and constantly evolving, reflecting the dynamic nature of technology and innovation.
What Is the Scope of ACM Computing Surveys?
The primary aim of ACM Computing Surveys is to publish comprehensive, peer-reviewed surveys that summarize and synthesize existing research on specific topics within computer science. These articles are not original research papers but rather deep, insightful overviews written by experts, which help readers understand the structure, trends, challenges, and future directions of a given area.
The scope includes (but is not limited to) the following key areas:
1. Artificial Intelligence and Machine Learning
CSUR features extensive surveys on AI subfields such as deep learning, natural language processing (NLP), reinforcement learning, computer vision, and ethical AI. These surveys help researchers stay updated on AI advancements and applications.
2. Software Engineering
The journal covers software development methodologies, programming languages, testing and verification, agile practices, and software architecture. Readers gain a detailed understanding of how software systems are built, maintained, and evolved.
3. Computer Systems and Architecture
Topics include computer organization, operating systems, high-performance computing, parallel and distributed systems, and embedded systems—offering foundational insights into how computing systems function at scale.
4. Human-Computer Interaction (HCI)
CSUR explores user interface design, usability testing, accessibility, and interaction techniques, providing researchers and designers with a strong base in HCI principles and innovations.
5. Data Science and Databases
Surveys on data mining, big data analytics, database systems, and knowledge discovery help readers understand how to store, process, and analyze large-scale data effectively.
6. Cybersecurity and Privacy
A growing area in CSUR’s scope, articles cover cryptography, intrusion detection systems, secure systems design, privacy-enhancing technologies, and risk management.
7. Networks and Communications
Topics include internet architecture, wireless networks, mobile computing, IoT, and network protocols. Surveys in this area are valuable for researchers and engineers working on digital infrastructure and connectivity.
8. Theory of Computation
This includes complexity theory, algorithm design, automata theory, and formal verification, offering a strong foundation for those interested in the theoretical aspects of computer science.
9. Emerging Technologies
CSUR increasingly includes cutting-edge areas such as quantum computing, blockchain, edge computing, and green computing—highlighting its commitment to innovation.
Who Benefits from the Scope of CSUR?
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Academics and researchers seeking authoritative literature reviews
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Graduate students needing comprehensive understanding of their research fields
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Industry professionals wanting to stay updated with technological trends
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Educators developing course materials and syllabi based on reliable resources
Recent Research Articles
Latest publications matched automatically by ISSN.
A Survey on Cyber Resilience in IoT Networks: Challenges, Mechanisms, and Future Directions
Foroozan Darbandeh, Muhammad Rizwan Asghar, Liqun Chen
2026-09-05 · DOI: 10.1145/3845986Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation
Steffen Eger, Yong Cao, Jennifer D'Souza, Andreas Geiger et al.
2026-09-05 · DOI: 10.1145/3845596A Survey of Large-Scale Out-of-Core Graph Processing
Xianghao Xu, Fang Wang, Yongli Cheng, Yucheng Zhang et al.
2026-11-30 · DOI: 10.1145/3838595A Survey on Cybersecurity Threats in Industrial and Information Technology Environments: Advancements and Resilience Through Network Steganographic Techniques
Przemysław Szary, Wojciech Mazurczyk, Luca Caviglione
2026-11-30 · DOI: 10.1145/3842734Quantifying the Knowledge in Deep Neural Networks: An Overview
Ioanna Valsamara, Ioannis Mademlis, Ioannis Pitas
2026-09-04 · DOI: 10.1145/3845595Vector Commitment Design, Analysis, and Applications: A Survey
Vir Pathak, Sushmita Ruj, Ron Vander Meyden
2026-11-30 · DOI: 10.1145/3833384A Tutorial on Gaussian Process Learning-based Model Predictive Control
Jie Wang, Youmin Zhang
2026-11-30 · DOI: 10.1145/3841464A Survey on Lightweight Deep Neural Network Architecture Design
Yong Li, Yuang Chen, Qiming Liang, Shuhan Lv et al.
2026-11-30 · DOI: 10.1145/3842661Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey
Bo Ni, Zheyuan Liu, Yongjia Lei, Leyao Wang et al.
2026-11-30 · DOI: 10.1145/3837074Advanced Security through Hardware Capabilities: A Comprehensive Survey on CHERI Technology
Bruno Sá, Donato Ferraro, Everton de Matos, Rafail Psiakis et al.
2026-11-30 · DOI: 10.1145/3833870Detecting Vulnerabilities in Embedded Systems via Taint Analysis: A Survey
Guohao Wu, Hongliang Liang, Luming Yin, Qiuping Yi et al.
2026-11-30 · DOI: 10.1145/3841632A Review of Neural Question Generation: Approaches, Challenges, and Future Directions
Shasha Guo, Liang Pang, Jing Zhang, Cuiping Li et al.
2026-09-03 · DOI: 10.1145/3843765Corrigendum: 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study
Marvin Wyrich, Justus Bogner, Stefan Wagner
2026-10-31 · DOI: 10.1145/3838282A State-Of-The-Art Review of Industrial Time Series Data Analysis: Methods and Applications
Lilan Liu, Yixiang Zhang, Yan-Ning Sun, Hongxia Cai et al.
2026-09-02 · DOI: 10.1145/3844497A Survey on Table Mining with Large Language Models: Challenges, Advancements and Prospects
Mingyue Cheng, Qingyang Mao, Qi Liu, Yitong Zhou et al.
2026-09-01 · DOI: 10.1145/3844608A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Jiawei Shao, Zijian Li, Wenqiang Sun, Tailin Zhou et al.
2026-09-01 · DOI: 10.1145/3844940A Survey of Symbol Name Recovery in Software Reverse Engineering
Hongcheng Fan, Jielun Wu, Xincheng He, Yang Feng et al.
2026-09-01 · DOI: 10.1145/3844948A Comprehensive Survey on Identifying Influential Nodes: From Structural Centrality-based to Learning-based Methods
Amir Sheikhahmadi, Laleh Tafakori, Mahdi Jalili
2026-09-01 · DOI: 10.1145/3844941Trustworthy Intelligent Vehicular Networks: A Survey
Xiao Zhang, Nishaant Madhankumar, Deniz Acikbas, Rohit Raval et al.
2026-09-01 · DOI: 10.1145/3844946A Survey on Transformer-Based Long-Range Dependency Modeling in Intelligent Document Understanding
Xumu Jiang, Yu Zhang, Alireza Abbasi
2026-08-30 · DOI: 10.1145/3844505Reviews
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Version History
April 19, 2025 at 5:55 pm
April 19, 2025