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RV 2026: International Conference on Runtime Verification

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截稿日期:
2026-06-16 Extended
通知日期:
2026-08-01
会议日期:
2026-10-06
会议地点:
Kingston, Ontario, Canada
届数:
CCF: C   ICORE: B   浏览: 25338   关注: 1   参加: 0

会伴指数 (CP-I)

63.1 / 100
全站第 491 名 / 共 5,682 个会议 · 前 9%

软件工程 第 63 / 247

学术认可 (35%)
72
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
89
社区关注 (10%)
19
资料公开度 (15%)
55

用到的输入: 收录等级：CCF C, ICORE B · 有据可查的届次：26 · 在会伴关注它的研究者：1 人 · 过去 24 个月打开过本页的研究者：4 人

公开资料里还缺: 历年录用率 (+4.5) · 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 80% —— 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 1.1 · 算于 2026-09-16

征稿

RV 2026 (International Conference on Runtime Verification) is a CCF C / ICORE B conference held in Kingston, Ontario, Canada on 2026-10-06. The paper submission deadline is 2026-06-16 (extended). Acceptance notifications are sent on 2026-08-01.

Objectives and Scope Runtime verification is concerned with the monitoring and analysis of the runtime behavior of software and hardware systems. Runtime verification techniques are crucial for system correctness, reliability, and robustness; they provide an additional level of rigor and effectiveness compared to conventional testing and are generally more practical than exhaustive formal verification. Runtime verification can be used prior to deployment, for testing, verification, and debugging purposes, and after deployment for ensuring reliability, safety, and security and for providing fault containment and recovery as well as online system repair. The topics of the conference include, but are not limited to: specification languages for monitoring monitor construction techniques program instrumentation logging, recording, and replay combination of static and dynamic analysis specification mining and machine learning over runtime traces monitoring techniques for concurrent and distributed systems runtime checking of privacy and security policies metrics and statistical information gathering program/system execution visualization fault localization, containment, resilience, recovery, and repair monitoring systems with learning-enabled components dynamic type checking runtime verification for autonomy and runtime assurance runtime verification for assurance cases out-of-distribution and anomaly detection in ML-based systems safe reinforcement learning runtime verification of large language model (LLM) agents
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