Submission Deadline: 7 March 2022
Publication: November/December 2022
In development and implementation of AI-enabled systems, the main challenge is not to develop the best models and algorithms, but to provide support for the entire lifecycle, from a business idea through data collection and management, software development managing both data and code, and product deployment and operation. There is a clear need for a support of software engineering (SE) for building AI that is beyond the standard SE methods and approaches. We name this emerging discipline “AI engineering,” as in SE for AI-intensive systems.
The aim of this special issue is to provide a venue for sharing practical experiences and research results on the new challenges that are emerging in SE and that AI/data-science engineers and software engineers are facing in development of AI-enabled systems (i.e., systems that include AI functions and components). Topics for this special issue include (but are not limited to):
- System and software requirements and their relation AI/ML modelling;
- Data management ensuring relevance and efficiency related to business goals;
- Data pipelines including design, reliability, anomaly detection, and robustness;
- Implications of edge AI and balancing intelligence between the cloud and edge devices;
- Self-healing data pipelines;
- Software architecture of development environments;
- System and software architecture of AI-enabled systems;
- Integration of AI-development processes and software development processes, including continuous and federated ML, continuous deployment, system, and software evolution;
- Ensuring and managing system and software nonfunctional properties and their relation to AI/ML properties, including run-time properties such as performance, safety, security, reliability, and life-cycle properties including reusability, maintainability, and evolution;
- Development teams, organizational and management issues for a successful development, and deployment of AI-enabled systems;
- Business, management, and organizational issues in building AI-enabled systems; and
- Novel approaches in automated building of AI-enabled systems, including run-time adaptation and automated retraining of ML models and/or creation of new ML models.
For author information and guidelines on submission criteria, please visit the Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Please contact the guest editors at email@example.com.
• Jan Bosch, Chalmers University of Technology, Sweden
• Helena Holmström Olsson, Malmö University, Sweden
• Björn Brinne, Peltarion, Sweden