CAIN 2026
Sun 12 - Sat 18 April 2026 Rio de Janeiro, Brazil
co-located with ICSE 2026
ICSE 2026 (series) / CAIN 2026 (series) /

CAIN Scope

Software Engineering for AI-Enabled Systems

The scope of CAIN is Software Engineering for AI-Enabled Systems. While there are many well-established venues in the fields of AI and machine learning (ML), CAIN is unique in that it takes a system and life cycle perspective on AI engineering, and we want to have a space for deep discussions, proposals, and solutions in this area. Therefore, all submissions MUST have a pronounced connection to software engineering, a system perspective, or a life cycle perspective.

We consider two types of submissions to be within the scope of CAIN:

  • AI System Submissions: these are submissions that fully fall within the scope of CAIN. In these submissions, software engineering methods and practices are applied/expanded/adapted for the engineering of AI-enabled systems, or novel techniques are introduced to promote system qualities of AI-enabled systems, e.g., fairness, explainability, or adaptability.
  • AI Component Submissions: these submissions apply software engineering methods and practices to an individual AI component, not to (larger parts of) the complete AI-enabled system, e.g., automated testing for software components containing ML models or API design of ML inference cloud services. Submissions focusing exclusively on the used AI algorithm, with no component, system, life cycle, or software engineering focus, will be desk-rejected, e.g., optimization of hyperparameter selection during ML model training.

Note that generic AI4SE submissions, i.e., studies that apply AI techniques to improve the engineering of arbitrary systems, are out of scope for CAIN. However, if a submission which focuses on SE4AI has AI as part of the SE solution, this is part of the scope for CAIN, e.g., a paper presents a multi-agent debate technique to improve requirements negotiation for AI-enabled systems, or a self-adaptive approach that uses AI for dynamic ML model selection during inference.

If you are still unsure whether your paper aligns with the scope of CAIN, please reach out to the program co-chairs.