Favorite Repositories

Below are repositories that we like their structure. More information can be found in the provided links.

Visual Studio Code

Added to list by Ömer Kırbıyık

Even though it is relatively new project, a huge amount of commits done, 29576 commits as of now.
They have a comprehensive guide to those who would like to contribute Visual Studio Code.
It's Wiki page is well-organized such that it contains Roadmap, Iteration Plans, Release Process, Related Projects.
They express the agenda of project clearly and put deadlines to tasks.
They have 511 contributors in this repository and the number of the contributors is increasing.
They indicate the ways of contributing including logging bugs, submitting pull requests, reporting issues, and creating suggestions.
They wants contributors to use labels in pull requests effectively in order to improve the chances for merge.For this reason, they suggest the help-wanted or bug labels in pull requests.
The issue section is used effectively. Issues are replied by the other developers quickly and the labels for issues are so clear that some who look this repository first can select and find the issue he searches easily.
https://code.visualstudio.com

Tensorflow is a great framework and open source software library to do numerical computations easily, especially in Deep Learning.
Tensorflow has a clear Readme that enables us to use framework easily. Also, it serves their library that is compatible with both GPU and CPU.
It has the large number of labels in label section which may cause to follow hard. But, labels are divided so clearly so that some understanding their usage use the issues section.
Also, there are scores of people contributing this open source library, which makes Tensorflow to be big platform.
They have 1337 contributors that is very good number for repository.
They give the links of their official website, their own released papers, and their courses in different platforms, which is very helpful for people who want to begin learning this framework.
In contrast, they have no wiki page explaining the process of this project and give clear and good information about it. For this reason, the only documentation of Tensorflow is Readme which is not give enough information to understand all process from past to present.
https://www.tensorflow.org/

Node is a rapidly growing repository making use of Javascript on the server side with high performance.
Since its launch in 2009, more than 11k pull requests accepted and more than 460 versions are released on GitHub.
It has a minimalist but detailed and well-defined labeling system.
There is a detailed documentation on the repository. Additionally, it has a large contribution community consisting of different languages as well.
1918 contributors participate to improve this project, which is very good number among open-source project.
21164 commits are done by developers so that they develop their projects and commit the improvements regularly. Therefore, we can see lots of checkpoint easily.
Node.js has 126 branches, that enables developers to handle version management easily. In addition, they entitle their version clearly.
https://nodejs.org/en/

Added to list by Atıf Emre Yüksel

Scikit-learn is a python module for machine learning built on the SciPy, which is library for scientific computation, and the project was in 2007 by David Cournapeau.
This is a Google Summer of Code project so that they are scores of contributors to develop this repository.
The Readme is very clear and useful to install and handle the dependency problem encountered by developers. Moreover, they serve help and support section in Readme in order to utilize the repository and its documentation.
They have merely 20 branches that make difficult to handle the version management and follow the new features adding to specific version.
They have great and well-organized wiki so that developers can easily search some details and the process of project on wiki. They create some useful template about how to write docs, how to make a release for developers, how to contribute this project.
They use the projects section in repository effectively and they specialize the titles of the cards in project in order to find more efficient way to handle the issues.
The labels using in issues section is not sufficient to manage this project completely and solve the problems quickly. They follow a different way to use labels according to the common names of labels.
http://scikit-learn.org/stable/