Información de la conferencia

RV 2026: International Conference on Runtime Verification

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RV
Día de Entrega:
2026-06-16 Extended
Fecha de Notificación:
2026-08-01
Fecha de conferencia:
2026-10-06
Ubicación:
Kingston, Ontario, Canada
Ediciones:
CCF: C   ICORE: B   Vistas: 24913   Seguidores: 1   Asistentes: 0

Solicitud de Artículos

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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