AI-driven optimization model for software requirement prioritization
Deepali Shahaneshahanedeepali@gmail.comDepartment of Computer Science and ApplicationsSchool of Computer Science & EngineeringDr. Vishwanath Karad MIT World Peace UniversityPune, Maharashtra, 411038, India0000-0002-3099-9009View full profile → , Babasaheb Dnyandeo Patilbabasaheb.d.patil@bharatividyapeeth.eduDepartment of Computer ApplicationsInstitute of Management & Rural Development AdministrationBharati Vidyapeeth (Deemed to be University)Sangli, Maharashtra, 416416, India0000-0002-6339-9595View full profile → , Prashant Kharatprashant.kharat@walchandsangli.ac.inDepartment of Information TechnologyWalchand College of EngineeringShivaji UniversitySangli, Maharashtra, 416415, India0000-0002-7111-2291View full profile → , *Manisha Shinde-PawarCorresponding authormanisha.pawar@ritindia.eduDepartment of Computer ApplicationsKasegaon Education Society’s Rajarambapu Institute of TechnologySakharale, Maharashtra, 415414, India0000-0002-8276-9782View full profile → , Vikas V. Patilvikas.patil@bharatividyapeeth.eduDepartment of Management StudiesYashwantrao Mohite Institute of ManagementBharati Vidyapeeth (Deemed to be University)Karad, Maharashtra, 415539, India0000-0003-2964-3838View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 31 Jul 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2366
- Pages:
- 2789–2806
Abstract
Manual Software requirements engineering has encountered significant challenges related to time overhead, high human effort, susceptibility to errors, and limited scalability for dynamic change handling, ranking and prioritization of non-functional requirements and requirements change requests. This study proposes an adaptive, dynamic, scalable, and AI-driven optimization model for software requirements scaling and prioritization using advanced machine learning and hybrid AI-based prioritization to improve accuracy and efficiency in requirement decision-making.
The proposed approach employs hybrid AI-driven framework integrating machine learning models for requirement classification and prediction, natural language processing for text processing, optimization-based scoring for ranking and prioritization, and domain-aware AI models. Software Requirements Prioritization and Change Management Model (SRPCMM) embed AI-driven analysis across requirement engineering phases, enabling optimization-based scoring and dynamic re-prioritization, Natural Language Processing (NLP) -based ambiguity reduction, and efficient change management. A novel risk-adjusted weighted priority scoring mechanism supports realistic and integrated criteria-driven evaluation. The experimental evaluation on an industrial software dataset has shown a 25–30% improvement in prioritization accuracy. The proposed model addresses key limitations of traditional requirement engineering by enabling integrated, automated, and intelligent decision-making processes.
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References
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