Studying the Health of Bitcoin Ecosystem in GitHub

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.


Osman, Khadija




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%.


Computer science




Carleton University

Thesis Degree Name: 

Master of Computer Science: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Computer Science

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).