Mario G.C.A. Cimino

Session 3: Function Point Analysis in the Age of AI: Rethinking Boundaries, Complexity and Effort
Time: 11:00 am – 11:30 am

Session Abstract:
Function Point Analysis assumes that elementary processes are repeatable, that logical data is user-recognizable, and that functional size is independent of the delivery technology. AI-based systems appear to violate all three. This presentation argues that much of the difficulty stems from boundary placement rather than from the technology itself.

The first part addresses the sizing of AI-based systems, weighing three strategies. Drawing a separate boundary around the AI component makes the inference service an identifiable elementary process, an EO, given its derived, algorithmic processing crossing the boundary and its knowledge base an EIF from the host, an ILF within. FPA counts unique elementary processes, not executions, so conversational volume does not inflate the count. The second strategy allocates AI-specific complexity to SNAP: Data Entry Validations for guardrails, Logical and Mathematical Operations for embedding and ranking. The third, more radical, adds a complementary dimension for model and uncertainty complexity.

One consequence of the boundary decision deserves attention: prompt templates, guardrail rules and retrieval configurations maintained by business users may qualify as user-maintained logical data, with the transactions maintaining them work now dismissed as invisible, yet countable under the unmodified standard.

The second part addresses software developed with AI. Technology independence implies size remains unchanged; the effect therefore concentrates in hours per function point, the term governing FP-based contracts and benchmark repositories. Output-based pricing must then decide who captures the productivity gain.

AI does not make functional sizing obsolete; it separates what is built, how it is built, and how complex it is to build.

Speaker Bio:
Mario G.C.A. Cimino professor in the Dept Information Engineering at the University of Pisa. His research focuses on the fields of Information Systems and Artificial Intelligence. He is the (co-)author of approximately 130 international scientific publications. He is associate editor of the international journals Granular Computing (Springer) and Ambient Intelligence and Humanized Computing (Springer). He is president of the IEEE CIS Task Force “Intelligent Agents,” IEEE Computational Intelligence Society. He was co-chair of the IEEE Symposium on Intelligent Agents (IEEE SSCI) in 2021 and 2022. He is a member of the Councils of the PhD program in Information Engineering and Smart Computing.

He is a member of the CINI National Lab in Artificial Intelligence and Intelligent Systems. He is President (elected) of the IEEE Technology and Engineering Management Society, Italy Chapter. He founded and coordinates the Machine Learning and Process Intelligence research group at the Dept of Information Engineering. He is co-founder of the innovative startup ribes.ai, operating in the field of humanoid robots and avatar software. He was a visiting scholar at the Department of Electrical & Computer Engineering at the University of Alberta, Canada. He was a visiting academic at the Cognitive Robotics and Autonomous Systems lab, School of Computing, University of Kent, UK.