Antonio Luca Alfeo

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:
Antonio Luca Alfeo is Associate Professor at eCampus University, where he holds the chairs of Programming Languages and Foundations of Artificial Intelligence and is a member of the Smartest Research Centre. He earned his PhD in Smart Computing from the Universities of Florence, Pisa and Siena, and previously served as Assistant Professor at the Department of Information Engineering, University of Pisa, where he co-taught Software Engineering and Software System Engineering.

His research addresses Explainable AI (XAI) and deep representation learning, applied to Industry 4.0 and to the analysis of physiological and behavioral data. He has authored over thirty peer-reviewed publications with an h-index of 14, and serves as Associate Editor for Expert Systems With Applications. He has been a visiting scholar at the MIT Media Lab and a visiting research fellow at the University of Essex, and contributes to the Italian PNRR research programers FAIR (Human-centered AI) and MOST.

Alongside his academic work, he acted as scientific coordinator of an industrial AI project for Körber Tissue, which delivered production software for anomaly detection and maintenance support in manufacturing environments. This combination of software engineering teaching and applied AI delivery informs his interest in how established measurement standards can accommodate probabilistic system components.