TY - GEN
T1 - Reviewing the Use of AI-Driven Functional Size Measurement
AU - Elouataoui, Widad
AU - Idri, Ali
AU - Abran, Alain
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2027.
PY - 2027
Y1 - 2027
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - COSMIC
KW - Functional Size
KW - Software Sizing
UR - https://www.scopus.com/pages/publications/105050434542
U2 - 10.1007/978-3-032-36587-3_5
DO - 10.1007/978-3-032-36587-3_5
M3 - Contribution to conference proceedings
AN - SCOPUS:105050434542
SN - 9783032365866
T3 - Lecture Notes in Computer Science
SP - 59
EP - 67
BT - Software Engineering and Advanced Applications - 52nd Euromicro Conference, SEAA 2026, Proceedings
A2 - Berger, Christian
A2 - Klös, Verena
PB - Springer Science and Business Media Deutschland GmbH
T2 - 52nd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2026
Y2 - 2 September 2026 through 4 September 2026
ER -