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

dc.contributor.authorCharatvaraphan R.
dc.contributor.authorChatchaiyadech B.
dc.contributor.authorSukijprasert T.
dc.contributor.authorRagkhitwetsagul C.
dc.contributor.authorChoetkiertikul M.
dc.contributor.authorKula R.G.
dc.contributor.authorSunetnanta T.
dc.contributor.authorMatsumoto K.
dc.contributor.correspondenceCharatvaraphan R.
dc.contributor.otherMahidol University
dc.date.accessioned2026-04-10T18:28:23Z
dc.date.available2026-04-10T18:28:23Z
dc.date.issued2025-01-01
dc.description.abstractAssessing 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-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.
dc.identifier.citationProceedings 2025 40th IEEE ACM International Conference on Automated Software Engineering Ase 2025 (2025) , 3997-4000
dc.identifier.doi10.1109/ASE63991.2025.00359
dc.identifier.scopus2-s2.0-105034677748
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/116082
dc.rights.holderSCOPUS
dc.subjectMathematics
dc.subjectComputer Science
dc.subjectEngineering
dc.titlePyGress: Tool for Analyzing the Progression of Code Proficiency in Python OSS Projects
dc.typeConference Paper
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105034677748&origin=inward
oaire.citation.endPage4000
oaire.citation.startPage3997
oaire.citation.titleProceedings 2025 40th IEEE ACM International Conference on Automated Software Engineering Ase 2025
oairecerif.author.affiliationThe University of Osaka
oairecerif.author.affiliationMahidol University
oairecerif.author.affiliationNara Institute of Science and Technology

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