Limaylla-Lunarejo, María-IsabelCondori Fernández, NellyRodríguez Luaces, Miguel2026-04-172026-04-172026-08M.I. Limaylla-Lunarejo, N. Condori-Fernandez, adn M. R. Luaces, "Automatic Criteria for Prioritizing Software Requirements in Spanish Projects", Journal of Systems and Software, Vol. 238, August 2026, 112858, https://doi.org/10.1016/j.jss.2026.1128581873-1228https://hdl.handle.net/2183/48027Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG The final version of the PrioReSpa dataset, including requirement descriptions, relevance label, F/NF classification, MoSCoW categorization, and Mandatory criteria, has been published on Zenodo. https://doi.org/10.5281/zenodo.15552978[Abstract]: Requirements prioritization is a complex process for determining the implementation order of requirements based on business value, cost, time, and other factors. Despite the growing use of AI to automate prioritization, full automation remains limited because common criteria, such as stakeholder ranking, cost, and dependencies, still require human input. To mitigate this, we focus on criteria that can be obtained automatically. We identified three such criteria: (1) Functional/Non-Functional (F/NF) classification, based on requirement purpose using pre-trained models; (2) MoSCoW categorization, which uses linguistic patterns and keywords to assess necessity; and (3) a Mandatory criterion derived from the similarity requirements process. In this study, we investigate the prioritization of requirements written in Spanish using automated criteria and automating the process with the LambdaMART algorithm. Additionally, we introduce PrioReSpa, a dataset of 401 requirements collected from a GIS project based on a software product line, each labeled with an importance score and the three prioritization criteria. Our findings demonstrate the effectiveness of using automated criteria for requirements prioritization. Results show that F/NF classification is the most influential criterion, followed by MoSCoW, while the Mandatory criterion had no impact. We trained LambdaMART models using XGBRanker and LGBMRanker implementations, performing hyperparameter optimization with Optuna. Both rankers obtained comparable NDCG performance, with LightGBM slightly outperforming in ranking metrics and XGBoost providing faster training. These findings demonstrate the viability of using LambdaMART algorithm to generate initial ranked lists of requirements, particularly for the top 10–20 priorities, reducing stakeholder involvement in early stages.engAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Requirements prioritizationAutomatic prioritization criteriaLambdaMARTjournal articleopen access10.1016/j.jss.2026.112858