# A Topological Analysis of Communication Channels for Knowledge Sharing in Contemporary GitHub Projects

## TL;DR

This paper analyzes how GitHub's communication channels facilitate knowledge sharing among developers, revealing their evolution, adoption patterns, and roles in externalizing and combining knowledge through a large-scale topological study.

## Contribution

It provides a novel topological analysis of GitHub communication channels, mapping their evolution and usage patterns in knowledge sharing within software projects.

## Key findings

- Projects adopt multiple communication channels.
- Channels evolve over time.
- Channels facilitate externalization and combination of knowledge.

## Abstract

With over 28 million developers, success of the GitHub collaborative platform is highlighted through an abundance of communication channels among contemporary software projects. Knowledge is broken into two forms and its sharing (through communication channels) can be described as externalization or combination by the SECI model. Such platforms have revolutionized the way developers work, introducing new channels to share knowledge in the form of pull requests, issues and wikis. It is unclear how these channels capture and share knowledge. In this research, our goal is to analyze these communication channels in GitHub. First, using the SECI model, we are able to map how knowledge is shared through the communication channels. Then in a large-scale topology analysis of seven library package projects (i.e., involving over 70 thousand projects), we extracted insights of the different communication channels within GitHub. Using two research questions, we explored the evolution of the channels and adoption of channels by both popular and unpopular library package projects. Results show that (i) contemporary GitHub Projects tend to adopt multiple communication channels, (ii) communication channels change over time and (iii) communication channels are used to both capture new knowledge (i.e., externalization) and updating existing knowledge (i.e., combination).