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.