AI-Native Software Engineering: Methodologies, Workflows, and Engineering Practices (AISEP)

The widespread adoption of generative AI is fundamentally changing software engineering. Despite the rapid adoption of AI-powered development tools, organizations continue to rely on ad hoc practices and fragmented workflows, with limited evidence regarding their effectiveness, reliability, and long-term sustainability. There is an increasing need for systematic engineering methodologies, standardized development processes, evaluation frameworks, and best practices that enable the development of robust, trustworthy, and maintainable AI-native software systems.

- Purpose of the special issue:
This special session aims to bring together researchers and practitioners working on the emerging foundations of AI-native software engineering, with emphasis on evidence-based methodologies, engineering processes, empirical studies, and industrial experiences that advance the maturation of AI-assisted software development.

- Topics of interest:
• AI-native software engineering methodologies and frameworks
• AI-assisted software development processes and workflows
• AI-driven requirements engineering
• AI-assisted software architecture and design
• AI-supported testing, verification, and validation
• Code quality and maintainability of AI-generated software
• Human-AI collaboration in software engineering
• Metrics and empirical evaluation of AI-assisted development
• AI governance, trustworthiness, and security
• Industrial case studies and best practices
• Standards, guidelines, and engineering frameworks for AI-native software development

The objective is to synergise software engineering principles with AI-driven approaches to address emerging challenges of engineering software-intensive systems and provide a forum for disseminating results and building a community of research on AI for SE.

- Organizers:
• Sokratis Karkalas - University of Derby, UK (contact: s.karkalas-AT-derby.ac.uk)
• Aakash Ahmad - University of Derby, UK
• Hong Qing (Harry) Yu - University of Derby, UK

- Dates:
• Submission Deadline: Nov 10, 2026
• Notification: Dec 10, 2026
• Date of the Session: Jan 15, 2027

- Further details: https://www.icsie.org/cfp_ss1.html