A master thesis from Aalborg University
Predictive Insights: Machine Learning and FOSS Project Sustainability
Author(s)
Term
4. Term
Publication year
2024
Submitted on
2024-06-13
Pages
58 pages
Abstract
This thesis explores the use of machine learning models to predict the graduation and retirement of Free and Open Source Software (FOSS) projects within the Apache Software Foundation Incubator (ASFI) using sustainability metrics. By training models with established sustainability indicators, we demonstrated predictive potential for ASFI projects. Key metrics impacting predictions included community size, development activity, growth, communication frequency, and turnover. Our findings highlight a link between sustainability and ASFI graduation and retirement, advocating for a combined qualitative and quantitative approach to improve our understanding and its prediction accuracy.
Keywords
FOSS ; Sustainabiilty ; Apache ; ASFI ; Machine-Learning ; ML ; Prediction
Documents
Colophon: This page is part of the AAU Student Projects portal, which is run by Aalborg University. Here, you can find and download publicly available bachelor's theses and master's projects from across the university dating from 2008 onwards. Student projects from before 2008 are available in printed form at Aalborg University Library.
If you have any questions about AAU Student Projects or the research registration, dissemination and analysis at Aalborg University, please feel free to contact the VBN team. You can also find more information in the AAU Student Projects FAQs.