1. Evaluating a programming topic using GitHub data: what we can learn about machine learning
- Author
-
Valentina Tortolini and Paolo Dello Vicario
- Subjects
Structure (mathematical logic) ,Computer Networks and Communications ,Computer science ,business.industry ,Deep learning ,Social impact ,Python (programming language) ,Machine learning ,computer.software_genre ,Open source ,Web mining ,Artificial intelligence ,business ,computer ,Mining software repositories ,Information Systems ,computer.programming_language ,Network analysis - Abstract
Purpose The purpose of this paper is to define a methodology to analyze links between programming topics and libraries starting from GitHub data. Design/methodology/approach This paper developed an analysis over machine learning repositories on GitHub, finding communities of repositories and studying the anatomy of collaboration around a popular topic such as machine learning. Findings This analysis indicates the significant importance of programming languages and technologies such as Python and Jupyter Notebook. It also shows the rise of deep learning and of specific libraries such as Tensorflow from Google. Originality/value There exists no survey or analysis based on how developers influence each other for specific topics. Other researchers focused their analysis on the collaborative structure and social impact instead of topic impact. Using this methodology to analyze programming topics is important not just for machine learning but also for other topics.
- Published
- 2021