CAIN 2026

Welcome to CAIN 2026 — 5th International Conference on AI Engineering – Software Engineering for AI

CAIN’26 is co-located with the 48th International Conference on Software Engineering (ICSE’26). Please find more information about ICSE’26 here.

The CAIN Conference Series aims to bring together researchers and practitioners in software engineering, data science, and artificial intelligence (AI) as part of a growing community targeting the challenges of software engineering for AI-enabled systems. In the development and implementation of AI-enabled systems, experience has shown that the main challenge is not to develop the best models or algorithms but rather to provide support for the entire system life cycle – from a business idea, through data collection, model training, system design and development, product deployment and operation, and finally system maintenance and evolution. Therefore, there is a clear need to advance the field of Software Engineering for AI (SE4AI). While there are many well-established venues in the fields of AI and machine learning (ML), CAIN is unique because it takes a system and life cycle perspective on AI engineering.

CAIN has the following goals:

• Identify and clearly describe the main challenges in SE4AI based on industrial needs and experiences, and contribute to solving them.
• Foster high-quality publications and discussions leading to research directions for AI engineering, the life cycle of AI-enabled systems, and the software engineering practices to support it.
• Contribute to a better understanding of the differences between data science and software engineering approaches, such that practices from both fields can come together to solve practical problems in AI engineering.
• Build a thriving community of software engineering, data science, and AI practitioners and researchers, who want to improve how we engineer AI-enabled systems.

CAIN 2026 will have sessions that combine submissions from multiple tracks:

• Invited keynotes and panels
• Presentations of accepted research and experience papers
• Industry talks
• Journal-first presentations
• Poster presentations
• Doctoral symposium presentations

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.

Note on Scope: Submissions that report predominantly on data science or AI/ML algorithms without any or only minor connection to software engineering for AI-enabled systems will be desk-rejected. There are many venues for data science and AI/ML papers, where authors would get much more valuable and relevant feedback. More details on the scope are available here.