Bibliographic record
Abstract
Bitcoin is a virtual and decentralized cryptocurrency that operates in a peer-to-peer network providing a private payment mechanism.It is a multi-billion dollar cryptocurrency, and hundreds of other cryptocurrencies are created based on it.Bitcoin is based on Open-Source (OSS) software development, and OSS is a convenient way to qualitatively measure software development and growth.This thesis presents the first comprehensive study of the Bitcoin ecosystem in GitHub organized around 481 most popular and actively developed Bitcoin related projects over eight years (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018).Our work includes manual and data-driven categorization of the projects, defining software health metrics, classification of projects into three different classes of health, and evaluation of trends in the health of the ecosystem as a whole.Four classification algorithms such as decision tree, Support Vector Machines, K-Nearest Neighbor and Naïve Bayes are leveraged to predict the health of a project and compare the classifiers' performance.The dataset used in this research is a combination of GHTorrent and a data collected during this study.The main findings suggest that the Bitcoin ecosystem in GitHub is represented by nine categories as result of manual categorization and 4 clusters based on the data-driven approach.Moreover, majority of the projects are assessed as "Low Risk".Our classification results show that decision trees outperform other classifiers in predicting the health of a software projects with an accuracy of 98%.iii I, Khadija Osman, would like to express my sincere gratitude to my amazing supervisor, Professor Olga Baysal, for her continuous guidance, advice, and friendly discussions throughout my thesis.Our joint vision for this project, and her continuous efforts provided me valuable feedback and support, which made this work successful.Besides my supervisor, I wish to express my deepest appreciation to all the thesis committee members for their valuable time, insightful comments and
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".