Conflict detection in software requirements is essential for ensuring specification consistency, improving project efficiency, and ensuring overall software quality. Despite its importance, research on this task, particularly for Arabic, remains limited due to the scarcity of annotated data and linguistic challenges. To address this gap, we introduce AraREQ, a large-scale Arabic dataset for requirement-level conflict detection and resolution. The dataset is constructed through a semi-automated Arabization process using Large Language Models (LLMs), followed by manual augmentation to address class imbalance. The final dataset comprises 27K Arabic requirement pairs. We benchmark four state-of-the-art LLMs under zero-shot and few-shot settings, establishing the first comprehensive evaluation for Arabic requirements conflict detection. Experimental results show that few-shot prompting consistently improves performance, particularly on the minority conflict class, demonstrating the effectiveness of example-based prompting. Finally, we introduce an end-to-end system that automatically detects potential conflicts in Arabic software requirements and generates resolution suggestions. All datasets, codes, and the end-to-end system are open-source and available at: https://sina.birzeit.edu/ArReqConflicts/

We present Qabas, a novel open-source Arabic lexicon designed for NLP applications. The novelty of Qabas lies in its synthesis of 110 lexicons. Specifically, Qabas lexical entries (lemmas) are assembled by linking lemmas from 110 lexicons. Furthermore, Qabas lemmas are also linked to 12 morphologically annotated corpora (about 2M tokens), making it the first Arabic lexicon to be linked to lexicons and corpora. Qabas was developed semi-automatically, utilizing a mapping framework and a web-based tool. Compared with other lexicons, Qabas stands as the most extensive Arabic lexicon, encompassing about 58K lemmas (45K nominal lemmas, 12.5K verbal lemmas, and 473 functional-word lemmas). Qabas is open-source and accessible online at https://sina.birzeit.edu/qabas