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ISSRE 2026: International Symposium on Software Reliability Engineering

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投稿締切日:
2026-04-10 締切済み
通知日:
2026-07-08
開催日:
2026-10-20
開催地:
Limassol, Cyprus
開催回数:
37
CCF: B   ICORE: A   QUALIS: A2   閲覧: 73201   フォロー: 80   参加: 15
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会伴インデックス (CP-I)

84.8 / 100
全 5,749 件中 第 138 位 · 上位 3%

ソフトウェア工学 分野 247 件中 第 18 位

学術的評価 (35%)
92
投稿の選択性 (20%)
81
開催回数 (20%)
98
コミュニティの注目度 (10%)
70
公開情報の充実度 (15%)
65

使用した入力: 収録ランク：CCF B, ICORE A, QUALIS A2 · 採択率：27.6%（記録のある 5 回の平均） · 確認できる開催回数：37 · 会伴でフォローしている研究者：80 人 · 過去 24 か月にこのページを開いた研究者：38 人

公開情報で不足しているもの: 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 100% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.2 · 算出日 2026-10-21

論文募集

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
最終更新：Dou Sun（）

採択率

平均採択率: 33% 14 年間 (1996–2012).

年投稿数採択数採択率(%)
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%

これを見た人はこちらも見ています

CCFICORECP-I略称正式名称投稿締切開催日
AA*97.2The Web ConferenceThe ACM Web Conference2026-10-182027-05-10
AA*96.4ACLAnnual Meeting of the Association for Computational Linguistics2027-01-042027-08-17

関連会議

関連ジャーナル

CCF正式名称インパクトファクター出版社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
BIEEE Transactions on Intelligent Transportation Systems8.4IEEE1524-9050

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