会议信息

ISSRE 2026: International Symposium on Software Reliability Engineering

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截稿日期:
2026-04-10 已截稿
通知日期:
2026-07-08
会议日期:
2026-10-20
会议地点:
Limassol, Cyprus
届数:
37
CCF: B   ICORE: A   QUALIS: A2   浏览: 73203   关注: 80   参加: 15
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会伴指数 (CP-I)

84.8 / 100
全站第 138 名 / 共 5,749 个会议 · 前 3%

软件工程 第 18 / 247

学术认可 (35%)
92
投稿选择性 (20%)
81
会议传承 (20%)
98
社区关注 (10%)
70
资料公开度 (15%)
65

用到的输入: 收录等级：CCF B, ICORE A, QUALIS A2 · 录用率：27.6%（有记录的 5 届的均值） · 有据可查的届次：37 · 在会伴关注它的研究者：80 人 · 过去 24 个月打开过本页的研究者：38 人

公开资料里还缺: 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-10-21

征稿

ISSRE 2026 (International Symposium on Software Reliability Engineering) is a CCF B / ICORE A / QUALIS A2 conference held in Limassol, Cyprus on 2026-10-20. The paper submission deadline is 2026-04-10. Acceptance notifications are sent on 2026-07-08.

The International Symposium on Software Reliability Engineering (ISSRE) is the leading conference on software reliability research and practice. ISSRE focuses on techniques and tools for assessing, predicting, and improving the reliability, safety, security, and resilience of software systems. As modern software increasingly integrates AI/ML components, operates autonomously, and spans cloud-to-edge environments, ensuring reliable system behavior is more critical than ever. Topics of Interest ISSRE 2026 invites high-quality contributions that advance the theory and practice of software reliability across contemporary software-intensive systems, including systems that incorporate AI/ML components. Topics of interest include, but are not limited to: Foundations of Reliability and Dependability Principles, models, metrics, empirical methods, and theories of software reliability, resilience, robustness, and safety Systematic approaches to fault prevention, fault removal, fault tolerance, and fault forecasting in modern software systems Testing and debugging, formal methods, model checking, static/dynamic analysis, verification, and runtime assurance Reliability in AI-Driven and Autonomic Systems Reliability engineering for AI-enabled, autonomous, self-adaptive, and cyber-physical systems Assurance, testing, verification, and certification of AI/ML components, including foundation and generative models Reliability of AI-generated code: validation, verification, explainability, defect analysis, and trustworthy automation of development tasks Impact of AI on software lifecycle processes (design, testing, evolution, operations, and quality management) AI Techniques for Reliability Engineering Machine learning for defect prediction, anomaly detection, debugging assistance, fault localization, and test automation Learning-based approaches to self-healing, resilience management, predictive maintenance, and reliability optimization Reliability governance in AI-driven DevOps pipelines, including transparency, interpretability, and auditability Software Reliability in Emerging System Domains Reliability assurance for cloud, edge, IoT, 5G/6G, cyber-physical, high-performance, and network softwarization environments Dependability of open-source ecosystems, data-driven pipelines, model hubs, and AI-assisted contributions Benchmarking, stress testing, workload modeling, and measurement frameworks for large-scale and AI-based systems Trustworthiness, Security, and Responsible Software Engineering Intersections of reliability with security, privacy, fairness, transparency, and regulatory compliance Societal, ethical, and human impacts of pervasive AI-enabled software systems Responsible governance of AI-based systems, including lifecycle assurance, auditability, and risk analysis Human-Centered, Empirical, and Reproducible Reliability Research Field studies, experience reports, user studies, and human factors in reliability engineering Public datasets, benchmark suites, reproducibility packages, and replication/negative-result studies Tooling, automation, continuous reliability monitoring, observability, and operational feedback loops
由 Dou Sun 最后更新于

录用率

平均录用率: 33% 14 年间 (1996–2012).

年份提交数录用数录用率(%)
20121253830.4%
20111062725.5%
20101304232.3%
2009842125%
20081162925%
2007782633.3%
20061023837.3%
2005983232.7%
20041203932.5%
20032004120.5%
2002733345.2%
20011003838%
1998873944.8%
19961034139.8%

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CCF全称影响因子出版商ISSN
CIEEE Transactions on Reliability5.7IEEE0018-9529
Software Testing Verification and Reliability1.2Wiley0960-0833
Microelectronics Reliability1.9Elsevier0026-2714
BSoftware & Systems Modeling3.2Springer1619-1366
BPattern Recognition7.6Elsevier0031-3203
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BInformation Sciences6.0Elsevier0020-0255
BComputer Networks4.7Elsevier1389-1286
BComputers & Security5.4Elsevier0167-4048
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