Compare Similar GitHub Projects with this GitHub Stats Tool

By the end of 2017, over 25 million project repositorieswere hosted on GitHub. We all know that the number is counting.

With so many options out there, it’s hard to decide which one is the best for you to follow.

Imagine that you interested in finding some open source projects in the Machine Learning field, you would be lost at sea when you see the number of search results from GitHub:

Even if you already know some top projects in this field, you still need to compare them in your own way and decide which one is the best fit for you.

And that’s where this simple GitHub Stats tool comes into play. It collects a bunch of project metrics so that you can get a general understanding of the popularity and maturity of each project.

You may use it as a starting point of project comparison.

Getting started

Follow the three steps below and you’ll get what you want in real-time:

1. Head to the GitHub repo of the tool
2. Enter as many projects as you need to check on
3. Hit the Update button beside each metric

Take the above mentioned machine learning field as an example:

General Repository Information

So basically these metrics can answer the following questions for you:

1. Which is the freshest project? tensorflow wins because its last push happened just an hour ago.
2. Which project gets the most attention? tensorflow wins bwcause it has the most watchers.

Popularity metrics

It’s common that you want to know which project gains the most popularity.

But how can you measure popularity?

By stars and forks each project has gained.

See the screenshots below for the comparison results:

Star

Fork

Apparently tensorflow gets the strongest momentum during the entire life cycle.

Maturity metrics

Besides popularity, you might also care about the maturity of a project, which is worth considering. Generally speaking, the more mature a project is, the more powerful as well as stable it is.

How to measure the maturity of a project?

By the most recent version, number of issues and number of pull requests.

See the screenshots below for the comparison results:

Release

Issues

Pull Requests

tensorflow and Pytorch have been maintained constantly while caffe is still at its 1.0 version with much fewer issues and pull requests than its two peers.

It’s your decision to make which to follow.

Conclusion

Data matters. I hope this little GitHub Stats tool can bring the insight needed for you. Also feel free to use it your own way.

Here’s the project repo again: https://vesoft-inc.github.io/github-statistics/

Enjoy!