Context-aware requirements prioritization using integrated regression learning with ordinal neural modeling and RoBERTa

Effective prioritization of software requirements is essential for reducing project risks, optimizing resource allocation, and ensuring timely delivery. Conventional approaches such as Analytic Hierarchy Process (AHP) and MoSCoW often suffer from subjectivity, inefficiency, and poor scalability, making them unsuitable for large-scale projects. Although machine learning (ML) based methods improve scalability, they frequently overlook critical contextual factors such as risk, urgency, implementation effort, and inter-requirement dependencies. To address this gap, this study proposes a new machine learning based context aware software requirements prioritization system. In the proposed system, a pre-trained RoBERTa model and an ordinal neural regression model are employed to infer contextual features including technical risk, complexity, urgency, business value, implementation effort, requirement stability, stakeholder criticality, security sensitivity, and inter-requirement dependencies directly from requirement statements. These inferred features are then used as inputs to a supervised multiple regression model (XGBoost), which generates continuous priority scores for each requirement, with higher scores reflecting higher implementation priority. To ensure transparency, SHAP-based feature attribution is applied for feature importance analysis, and a feedback integration mechanism allows stakeholders to iteratively refine prioritization outcomes, thus in-turn retraining the core prioritization model. Empirical validation against three domain experts across five projects from different application domains demonstrates strong alignment, with Spearman rank correlations between 0.6 and 0.75, Mean Absolute Error (MAE) around 0.10, and Top 5 Match Rates up to 0.80. The results confirm that the proposed system provides a scalable, explainable, and context aware requirements prioritization mechanism suitable for real-world software engineering projects.

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