Studying the Health of Bitcoin Ecosystem in GitHub
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Bitcoin is a virtual cryptocurrency, that operates in a peer-to-peer network. This thesis presents the first comprehensive study of the Bitcoin ecosystem in GitHub organized around 481 Bitcoin-related projects over eight years (2010-2018). Our work includes manual and data-driven categorization of the projects, defining software health metrics, classification of the projects into three different classes of health, and evaluation of trends in the health of the ecosystem. Four classification algorithms such as Decision tree, Support Vector Machines, K-Nearest Neighbor, and Naive Bayes are leveraged to predict the health of a project. The dataset is a combination of GHTorrent and a dataset collected during this study. The main findings suggest that the Bitcoin ecosystem in GitHub is represented by nine categories by manual categorization and 4 clusters based on the data-driven approach. Moreover, most of the projects are assessed as "Low Risk" and decision tree outperforms with an accuracy of 98%.
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Copyright © 2020 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2020
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osman-studyingthehealthofbitcoinecosystemingithub.pdf | 2023-05-05 | Public | Download |