Visualizing Contributor Code Competency for PyPI Libraries: Preliminary Results

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 files contain more basic competency files, and that not every contributor contributes competent code. We show how~our visualization is able to summarize such information, and opens up different possibilities for understanding how to make elegant contributions.