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Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework

Articolo
Data di Pubblicazione:
2024
Abstract:
The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive deduplicated small files thanks to the use of a Merkle tree as the underlying data structure, querying the SWH dataset presents challenges due to the nature of these structures, which organize content based on hash values rather than any locality principle. The magnitude of the repository, coupled with the resource-intensive nature of the download process, highlights the need for specialized infrastructure and computational resources to effectively handle and study the extensive dataset housed within SWH. Currently, there is a lack of infrastructures specifically tailored for running analytics on the SWH dataset, leaving users to handle these issues manually. To address these challenges, we implemented the SWH-Analytics (SWHA) framework, a development environment that transparently runs custom analytic applications on publicly available software data preserved over time by SWH. Specifically, this work shows how SWHA can be effectively exploited to study usage patterns of free and open-source software licenses, highlighting the need to improve license literacy among developers.
Tipologia CRIS:
03A-Articolo su Rivista
Keywords:
Free and open-source software; Large-scale analytics; License conflicts; License management; Software Heritage
Elenco autori:
Antelmi A.; Torquati M.; Corridori G.; Gregori D.; Polzella F.; Spinatelli G.; Aldinucci M.
Autori di Ateneo:
ALDINUCCI Marco
ANTELMI Alessia
Link alla scheda completa:
https://iris.unito.it/handle/2318/1975935
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/1975935/1294919/Antelmi_JSUPE2024.pdf
Pubblicato in:
THE JOURNAL OF SUPERCOMPUTING
Journal
Progetto:
Third Party CINI - "ADMIRE - Adaptive multi-tier intelligent data manager for Exascale" - Call H2020-JTI-EuroHPC-2019-1 - Grant Agreement n. 956748 - CUP F69J21003450007
  • Dati Generali
  • Aree Di Ricerca

Dati Generali

URL

https://link.springer.com/article/10.1007/s11227-024-06069-x

Aree Di Ricerca

Settori (7)


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