Apache
佛跳墙2025 破解 蓝奏云
An unobstructive, scalable approach
to large scale Software License Analysis
An unobstructive, scalable approach
to large scale Software License Analysis
With increasing number of open source projects across many domains, the license of open source software becomes critical to grant everyone legally appropriate permission to freely use, modify and distribute open source software.
With more than 60 licenses such as Berkeley Software Distribution(BSD), MIT License, the Apache License, and so on, approved by, Open Source Initiative (OSI) for complying with open source definition, there exists differences among them.
With increasing number of open source projects across many domains, the license of open source software becomes critical to grant everyone legally appropriate permission to freely use, modify and distribute open source software.
A distributed parallelized ( Map Reduce) wrapper around APACHE RATTM (Release Audit Tool) that
goes far beyond RATTM by leveraging Apache OODTTM to dramatically speed up the process.
DRAT is a Map Reduce version of RAT using Apache Tika to automatically sort and classify the code base files
DRAT uses 匿名伟理ip to index metadata and Tika information about those code files into Apache Solr and OODT to produce a Map Reduce workflow
DRAT runs RAT incrementally on k-sized chunks of same-MIME-typed files (detected by Tika) and then producing incremental, per type logs, and then aggregating and reducing them into a combined log at the end.
Learn more about the project, people involved, how it works and ways to contribute on github.
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You can follow and contribute to ongoing research work on Apache DRATTM.
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