The goal of the CAIN Conference Series is to bring together researchers and practitioners in software engineering, data science, and artificial intelligence (AI) as part of a growing community that is targeting the challenges of Software Engineering for AI-enabled systems.
In development and implementation of AI-enabled systems — defined as systems that contain AI components — 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 business idea, through data collection, model training, system design and development, product deployment and operation, and eventually its maintenance and evolution. Therefore there is a clear need to advance the field of Software Engineering for AI. While there are many well-established venues in the fields of AI and machine learning (ML), CAIN is unique in that it takes a systems and life cycle perspective on AI engineering.
CAIN has the following goals for the next few years.
- Identify the main challenges in software engineering for AI systems, considering industrial needs and experience.
- Create a roadmap that captures research directions in AI engineering in relation to AI-enabled systems life cycle 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.
- Identify industrial challenges in building and using AI-enabled systems and contribute to solving them.
- Build a thriving community of software engineering, data science, and AI practitioners and researchers
CAIN’24 will have all sessions in a single track:
- Invited keynotes and panels.
- Presentation of accepted research and experience papers.
- Industry talks.
- Poster presentations
The area of interest for CAIN is Software Engineering for AI — improving the development of AI-based systems throughout the full life cycle. Topics include but are not limited to:
- System and software requirements and their relationship to AI/ML modeling.
- Data management ensuring relevance and efficiency related to business goals.
- System and software architecture for AI-enabled systems.
- Integration of AI and software development processes into the AI system development life cycle, including continuous integration and deployment, and system and software evolution.
- Ensuring and managing system and software nonfunctional properties and their relationship to AI/ML properties, including runtime properties such as performance, safety, security, and reliability; and life-cycle properties including reusability, maintainability and evolution.
- Collaboration, organizational, and management practices for a successful development of AI-enabled systems.
- Building effective infrastructures to support development of AI systems and components.
Note: Submissions that report strictly on data science or model development without any connection to software engineering and AI-enabled systems will be desk-rejected. As stated earlier, there are many venues for those papers where authors would get much more valuable and relevant feedback.
The CAIN’24 conference is co-located with the 46th International Conference on Software Engineering (ICSE’24). Please find more information about ICSE’24 here.
Stay tuned for more Information!