Visualizing Contributor Code Competency for PyPI Libraries: Preliminary Results
Indira Febriyanti, Raula Gaikovina Kula, Ruksit Rojpaisarnkit,, Kanchanok Kannee, Yusuf Sulistyo Nugroho, Kenichi Matsumoto

TL;DR
This paper introduces a visualization method to analyze and understand the relationship between contributor proficiency, code complexity, and file contributions in PyPI libraries, revealing that most files contain basic code and not all contributors produce advanced code.
Contribution
It presents a novel visualization approach to identify and analyze the complexity and proficiency of code contributions across different files and contributors in PyPI projects.
Findings
Most files contain basic competency code
Not all contributors contribute proficient code
Visualization helps summarize contributor and code complexity relationships
Abstract
Python is known to be used by beginners to professional programmers. Python provides functionality to its community of users through PyPI libraries, which allows developers to reuse functionalities to an application. However, it is unknown the extent to which these PyPI libraries require proficient code in their implementation. We conjecture that PyPI contributors may decide to implement more advanced Pythonic code, or stick with more basic Python code. Are complex codes only committed by few contributors, or only to specific files? The new idea in this paper is to confirm who and where complex code is implemented. Hence, we present a visualization to show the relationship between proficient code, contributors, and files. Analyzing four PyPI projects, we are able to explore which files contain more elegant code, and which contributors committed to these files. Our results show that most…
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Taxonomy
TopicsSoftware Engineering Research · Computational Physics and Python Applications · Scientific Computing and Data Management
