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PyGress: Tool for Analyzing the Progression of Code Proficiency in Python OSS Projects

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Conference object 16 Nov 2025Embargo end date: 01 Jan 2025Publisher:IEEEJournal:2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE)
Authors: Rujiphart Charatvaraphan; Bunradar Chatchaiyadech; Thitirat Sukijprasert; Chaiyong Ragkhitwetsagul; Morakot Choetkiertikul; Raula Gaikovina Kula; Thanwadee Sunetnanta; +1 Authors

doi: 10.1109/ase63991.2025.00359 , 10.48550/arxiv.2511.05821

arXiv: 2511.05821

PyGress: Tool for Analyzing the Progression of Code Proficiency in Python OSS Projects

Abstract

Assessing developer proficiency in open-source software (OSS) projects is essential for understanding project dynamics, especially for expertise. This paper presents PyGress, a web-based tool designed to automatically evaluate and visualize Python code proficiency using pycefr, a Python code proficiency analyzer. By submitting a GitHub repository link, the system extracts commit histories, analyzes source code proficiency across CEFR-aligned levels (A1 to C2), and generates visual summaries of individual and project-wide proficiency. The PyGress tool visualizes per-contributor proficiency distribution and tracks project code proficiency progression over time. PyGress offers an interactive way to explore contributor coding levels in Python OSS repositories. The video demonstration of the PyGress tool can be found at https://youtu.be/hxoeK-ggcWk, and the source code of the tool is publicly available at https://github.com/MUICT-SERU/PyGress.

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Keywords

Software Engineering (cs.SE), FOS: Computer and information sciences, Software Engineering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green