Limaylla-Lunarejo, María-IsabelCondori Fernández, NellyRodríguez Luaces, Miguel2025-06-112025-06-112025-05-14M. I. Limaylla-Lunarejo, N. Condori Fernández, and M. Rodríguez Luaces, "SimRE: A Requirements Similarity Tool for Software Product Lines", Proceedings of the 40th ACM Symposium on Applied Computing (SAC '25, ACM/SIGAP), 2025, DOI: 10.1145/3672608.3707758http://hdl.handle.net/2183/42273Presented at: 40th ACM Symposium on Applied Computing (SAC '25, ACM/SIGAP), 2025© 2025 Authors | ACM . This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SAC '25: Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, https://doi.org/10.1145/3672608.3707758Datasets dispoñibles en: https://doi.org/10.5281/zenodo.14506521[Abstract]: A Software Product Line (SPL) is a paradigm that effectively describes families of products based on reuse. Requirements engineering in this domain is a complex task, especially when new products are introduced. In this context, identifying similarities between new and existing requirements can help avoid additional effort and duplication. On the other hand, with the accelerated progress of deep learning in textual analysis, particularly through pre-trained models, several opportunities for semantic analysis have emerged, including semantic similarity. However, most of the research in this area has focused on the English language, with limited attention given to other languages, such as Spanish. In this paper, we introduce a novel tool, SimRE, that helps SPL engineers automatically identify the similarity of requirements written in Spanish using multilingual pre-trained models. We conducted a benchmarking study to validate and compare the performance of pre-trained models within the tool. This analysis aimed to provide a better understanding of their effectiveness in the Spanish language, particularly in the context of a Geographic Information System (GIS) product line. Copyright © 2025 held by the owner/author(s).eng© 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.Pre-trained modelsRequirements similaritySoftware product lineconference outputopen access10.1145/3672608.370775