OSS License Identification at Scale: A Comprehensive Dataset Using World of Code
<|reference_start|>OSS License Identification at Scale: A Comprehensive Dataset Using World of Code: The proliferation of open source software (OSS) has led to a complex landscape of licensing practices, making accurate license identification crucial for legal and compliance purposes. This study presents a comprehensive analysis of OSS licenses using the World of Code (WoC) infrastructure. We employ an exhaustive approach, scanning all files containing ``license'' in their filepath, and apply the winnowing algorithm for robust text matching. Our method identifies and matches over 5.5 million distinct license blobs across millions of OSS projects, creating a detailed project-to-license (P2L) map. We verify the accuracy of our approach through stratified sampling and manual review, achieving a final accuracy of 92.08%, with precision of 87.14%, recall of 95.45%, and an F1 score of 91.11%. This work enhances the understanding of OSS licensing practices and provides a valuable resource for developers, researchers, and legal professionals. Future work will expand the scope of license detection to include code files and references to licenses in project documentation.<|reference_end|>
@article{jahanshahi2024oss,
    title={OSS License Identification at Scale: A Comprehensive Dataset Using World
  of Code},
    author={Mahmoud Jahanshahi, David Reid, Adam McDaniel, Audris Mockus},
    journal={arXiv preprint arXiv:2409.04824},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04824},
    primaryClass={cs.SE}
}