Using the uniqueness of global identifiers to determine the provenance of Python software source code

• Published in: Empirical software engineering : an international journal Vol. 28; no. 5; p. 107
• Main Authors: Sun, Yiming, German, Daniel, Zacchiroli, Stefano
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2023; Springer Nature B.V; Springer Verlag
• Subjects: Compilers; Computer Science; Interpreters; Mathematical analysis; Names; Open source software; Programming Languages; Software Engineering; Software Engineering/Programming and Operating Systems; Software packages; Source code; Source code tracking; Identifiers; Open source software; Software provenance; Python; python; software provenance; source code tracking; open source software; identifiers
• DOI: 10.1007/s10664-023-10317-8
• ISSN: 1382-3256

We consider the problem of identifying the provenance of free/open source software (FOSS) and specifically the need of identifying where reused source code has been copied from. We propose a lightweight approach to solve the problem based on software identifiers—such as the names of variables, classes, and functions chosen by programmers. The proposed approach is able to efficiently narrow down to a small set of candidate origin products, to be further analyzed with more expensive techniques to make a final provenance determination. By analyzing the PyPI (Python Packaging Index) open source ecosystem we find that globally defined identifiers are very distinct. Across PyPI’s 244  K packages we found 11.2  M different global identifiers (classes and method/function names—with only 0.6% of identifiers shared among the two types of entities); 76% of identifiers were used only in one package, and 93% in at most 3. Randomly selecting 3 non-frequent global identifiers from an input product is enough to narrow down its origins to a maximum of 3 products within 89% of the cases. We validate the proposed approach by mapping Debian source packages implemented in Python to the corresponding PyPI packages; this approach uses at most five trials, where each trial uses three randomly chosen global identifiers from a randomly chosen python file of the subject software package, then ranks results using a popularity index and requires to inspect only the top result. In our experiments, this method is effective at finding the true origin of a project with a recall of 0.9 and precision of 0.77.