Automated analysis and specification of software requirements expressed in natural language is a challenge addressed by the research community and is becoming a reality thanks to the advances in Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques. While the research community focuses mainly on generic software requirements or specialized solutions for security requirements, we find a gap in the automation of analysis and specification for requirements in the cloud computing domain and the automatic mapping of requirements on actual products offered in the cloud service market. In this research work, we propose AI-CRAS an AI-driven cloud service requirement analysis and specification methodology. The proposed method, which leverages state-of-the-art transformer-based large language model, has been implemented and validated in a real case. Experimental results demonstrate that the model performed well in binary and multi-label classification of requirements (achieving recall/F1-score of 0.96/0.92 and 0.86/0.76, respectively) and mapping requirements into actual cloud services.
AI-CRAS: AI-driven Cloud Service Requirement Analysis and Specification / Casalicchio, Emiliano; Cotumaccio, Alberto. - (2024), pp. 11-21. ( 12th IEEE International Conference on Cloud Engineering, IC2E 2024 cyp ) [10.1109/ic2e61754.2024.00009].
AI-CRAS: AI-driven Cloud Service Requirement Analysis and Specification
Casalicchio, Emiliano
Primo
Writing – Original Draft Preparation
;
2024
Abstract
Automated analysis and specification of software requirements expressed in natural language is a challenge addressed by the research community and is becoming a reality thanks to the advances in Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques. While the research community focuses mainly on generic software requirements or specialized solutions for security requirements, we find a gap in the automation of analysis and specification for requirements in the cloud computing domain and the automatic mapping of requirements on actual products offered in the cloud service market. In this research work, we propose AI-CRAS an AI-driven cloud service requirement analysis and specification methodology. The proposed method, which leverages state-of-the-art transformer-based large language model, has been implemented and validated in a real case. Experimental results demonstrate that the model performed well in binary and multi-label classification of requirements (achieving recall/F1-score of 0.96/0.92 and 0.86/0.76, respectively) and mapping requirements into actual cloud services.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


