Información de la conferencia

CAIN 2026: International Conference on AI Engineering – Software Engineering for AI

Por favor Iniciar sesión para ver el sitio web de la conferencia
Cuenta gratuita: consulta los sitios oficiales, sigue las fechas límite y recibe recordatorios por correo.
Iniciar sesión Registrarse
Insertar insignia de cuenta atrás
CAIN
Día de Entrega:
2025-10-16
Fecha de Notificación:
2026-01-05
Fecha de conferencia:
2026-04-12
Ubicación:
Rio de Janeiro, Brazil
Ediciones:
5
ICORE: B   Vistas: 3104   Seguidores: 0   Asistentes: 0

Solicitud de Artículos

CAIN 2026 (International Conference on AI Engineering – Software Engineering for AI) is a ICORE B conference held in Rio de Janeiro, Brazil on 2026-04-12. The paper submission deadline is 2025-10-16. Acceptance notifications are sent on 2026-01-05.

Scope and Topics of Interest The area of interest for CAIN is Software Engineering for AI-Enabled Systems, i.e., systems that contain at least one AI component. An AI component is a software component that uses at least one AI technique to provide (parts of) its functionality, such as ML models, generative AI like large language models (LLMs), reinforcement learning, symbolic AI, AI planning, evolutionary algorithms, etc. CAIN focuses on a system and/or life cycle perspective. Relevant topics therefore include, but are not limited to: Requirements engineering for AI-enabled systems, e.g., elicitation, specification, or management, and the relationship of requirements to AI/ML model development. Data management for AI-enabled systems to ensure relevance and efficiency related to stakeholder goals. System and software architecture for AI-enabled systems, e.g., architecture modeling, architectural tactics, architecture/design patterns, or reference architectures. Integration of AI and software development activities into the AI engineering life cycle, e.g., continuous integration and deployment, operation and monitoring, and system and software evolution. Assurance and management of system quality attributes and their relationship to AI/ML properties, including runtime properties such as performance efficiency, safety, security, and reliability; and life cycle properties such as reusability, maintainability, evolvability, and observability. Collaboration, organizational, and management practices for the successful engineering of AI-enabled systems. Building effective infrastructures to support the development and operation of AI-enabled systems and components. Software engineering methods and tools for next-gen AI-enabled systems, e.g., systems that integrate foundation models or AI agents.
Última actualización por Dou Sun el

Conferencias Relacionadas

Revistas Relacionadas

CCFNombre CompletoFactor de ImpactoEditorISSN
AArtificial Intelligence4.6Elsevier0004-3702
AIMDPI2673-2688
AI & SOCIETY4.7Springer0951-5666
AI+ELSP3007-7443
AI & MaterialsELSP3006-7588
Frontiers in Robotics and AI3.0Frontiers Media S.A.2296-9144
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340

Comentarios 0

Aún no hay comentarios.

Por favor Iniciar sesión para publicar un comentario