Reviewing the Use of AI-Driven Functional Size Measurement

In recent years, the integration of Artificial Intelligence (AI) techniques into Functional Size Measurement (FSM) has gained increasing attention within the software engineering community. AI-based FSM methods aim to overcome the limitations of traditional approaches, namely, reliance on expert judgment and structured inputs, by enabling automation and scalability. This study presents a systematic literature review of research efforts that apply AI techniques to FSM. The review analyses 27 primary studies published between 2008 and 2025, sourced from major digital libraries. Various aspects are investigated, including publication trends, AI technique categories, functional sizing methods, and research types. The findings show a predominance of COSMIC as the preferred FSM method and highlight NLP, and transformer-based models as the most commonly employed AI techniques. While hybrid models combining rule-based systems with deep learning show promise, the field remains fragmented and underexplored. This review offers the first consolidated overview of AI-driven FSM research, identifying key trends, challenges, and directions for future investigation.