English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We consider the problem of identifying the provenance of free/open sourcesoftware (FOSS) and specifically the need of identifying where reused sourcecode has been copied from. We propose a lightweight approach to solve theproblem based on software identifiers-such as the names of variables, classes,and functions chosen by programmers. The proposed approach is able toefficiently narrow down to a small set of candidate origin products, to befurther analyzed with more expensive techniques to make a final provenancedetermination.By analyzing the PyPI (Python Packaging Index) open sourceecosystem we find that globally defined identifiers are very distinct. AcrossPyPI's 244 K packages we found 11.2 M different global identifiers (classes andmethod/function names-with only 0.6% of identifiers shared among the two typesof entities); 76% of identifiers were used only in one package, and 93% in atmost 3. Randomly selecting 3 non-frequent global identifiers from an inputproduct is enough to narrow down its origins to a maximum of 3 products within89% of the cases.We validate the proposed approach by mapping Debian sourcepackages implemented in Python to the corresponding PyPI packages; thisapproach uses at most five trials, where each trial uses three randomly chosenglobal identifiers from a randomly chosen python file of the subject softwarepackage, then ranks results using a popularity index and requires to inspectonly the top result. In our experiments, this method is effective at findingthe true origin of a project with a recall of 0.9 and precision of 0.77.

Using the Uniqueness of Global Identifiers to Determine the Provenance of Python Software Source Code