Year: 2019
Type: dissertation
Abstract: GitHub is an excellent democratic source of software. Unlike traditional work groups \nhowever, GitHub repositories are primarily anonymous and virtual. \nTraditional strategies for improving the productivity of a work group often include \nexternal consultation agencies that do in-person interviews. The resulting data from these \ninterviews are then reviewed and their recommendations provided. This is one such claim of \na group of strategies called group dynamics. In the online world however where colleagues \nare often anonymous and geographically dispersed, it is often impossible to apply such \napproaches. \nWe developed experimental methods to discern the same information that one would \nnormally obtain through in-person interviews through automated means. Here we provide \nthis automated method of data collection and analysis that can later be applied for the \npurposes of recommendation agents. \nComments from individual developers were collected via various GitHub APIs. That \ndata was then converted into personality traits for each individual through textual persona \nextraction and mapped to a personality space called SYMLOG. The resulting dynamics \nbetween each of the personalities of the developers of each repository are analyzed though \nSYMLOG to predict how successful each project is likely to be. These predictions are \ncompared against valid preexisting success metrics.
Source: UWSpace (University of Waterloo)
Author: Alexander Sachs
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2976667944