🤖 AI Summary
Public datasets in the LLM4RE (Large Language Models for Requirements Engineering) domain are fragmented, poorly documented, and lack systematic description, hindering comparability and reuse. Method: We conduct the first systematic dataset mapping study in LLM4RE, analyzing 62 publicly available datasets drawn from 43 scholarly publications along dimensions including document type, granularity, RE task phase, domain, and language. We propose the first domain-specific dataset classification and characterization framework for LLM4RE. Contribution/Results: Our framework identifies critical research gaps—particularly in requirements elicitation, requirements management, and multilingual support—and we release an open dataset catalog alongside a standardized featureization schema. This work significantly enhances dataset visibility, structural consistency, and cross-study comparability, laying the foundation for a unified benchmarking repository in LLM4RE.
📝 Abstract
[Context] Large Language Models (LLMs) rely on domain-specific datasets to achieve robust performance across training and inference stages. However, in Requirements Engineering (RE), data scarcity remains a persistent limitation reported in surveys and mapping studies. [Question/Problem] Although there are multiple datasets supporting LLM-based RE tasks (LLM4RE), they are fragmented and poorly characterized, limiting reuse and comparability. This research addresses the limited visibility and characterization of datasets used in LLM4RE. We investigate which public datasets are employed, how they can be systematically characterized, and which RE tasks and dataset descriptors remain under-represented. [Ideas/Results] To address this, we conduct a systematic mapping study to identify and analyse datasets used in LLM4RE research. A total of 62 publicly available datasets are referenced across 43 primary studies. Each dataset is characterized along descriptors such as artifact type, granularity, RE stage, task, domain, and language. Preliminary findings show multiple research gaps, including limited coverage for elicitation tasks, scarce datasets for management activities beyond traceability, and limited multilingual availability. [Contribution] This research preview offers a public catalogue and structured characterization scheme to support dataset selection, comparison, and reuse in LLM4RE research. Future work will extend the scope to grey literature, as well as integration with open dataset and benchmark repositories.