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How to track the popularity of GitHub projects, in response to this problem, this article details the corresponding analysis and solutions, hoping to help more small partners who want to solve this problem find a simpler and easier way.

GitHub is the world's largest open source software hosting platform, so tracking GitHub popularity is important for both software developers and users.

measuring prevalence

Here we consider GitHub's top 24 programming languages, classified by GitHub's advanced search engine. The data below is the distribution of stars in the top 1000 libraries for each language.

Suppose that out of 24000 sample systems, the top 10% are popular and the top 1% are very popular.

The following table shows the number of popular and very popular systems for each programming language

Popularity Growth Patterns

To assess app popularity over time, we limited the analysis to a prevalence system time of at least 52 weeks. In this way, we studied 2138 epidemic systems (89% of the initial samples). For the purposes of this system, we define Rt as the rank logarithm (base 2) of a ranked list in weeks t. The logarithm of this ranking is determined by the right-skewed number of Stars of the popular system. *** The system ranking is 1. The earliest week is 1 and *** is 52. RTop and RBottom are also defined as *** ranking and *** ranking.

We found several patterns of growth in popularity:

Continuous Growth: Continuous growth over the analysis period, calculated as:

(RBottom − RTop )

Rapid Growth: Calculation:

(ROld − RNow ) > 1 ∧ (Rt+1 ≤ Rt) in at least 90% of the weeks t

Examples:

Slow Growth: Calculated by:

(RNow − ROld) > 1 ∧ (Rt+1 ≥ Rt) in at least 90% of the weeks t

Example (which resulted in a decrease on their rank):

Viral growth: The number of Stars reaching *** in a short period of time (e.g., a week)

Examples:

The following list shows how each programming language continues to grow, fast, slow, and viral:

Prevalence Related Forks and Related Uses

Forks: The following data shows the relationship between the popularity of an item and the number of forks of TA. We can see a strong positive correlation between forks and Stars (Spearman rank correlation coefficient = 0.55).

Client-side: To correlate client-side usage with Stars, we focused on a restricted application grouping consisting of the Node.js base library of the NPM registry. We first use the NPM API to retrieve the number of popular JavaScript app dependencies in the database. Then manually review the top 100 apps based on the Node.js base library modules that depend on the selection. We also found a strong correlation between these two factors (Spearman's rank correlation coefficient of 0.68).

summary

We are working on a framework that tracks GitHub popularity, and using this framework we can discover:

JavaScript dominates more than a third of GitHub's popular apps, followed by Ruby, Objective-C, Python, Java and PHP, which account for another third.

21% of epidemic systems have sustainable growth;5% of epidemic systems have rapid growth; and less than 1% of epidemic systems have slow growth. We found 37 systems with virally growing behavior.

The number of Stars of a system is not only related to the number of forks, but also to the efficient use of other client applications.

About how to track the popularity of GitHub project answers to the question shared here, I hope the above content can be of some help to everyone, if you still have a lot of doubts not solved, you can pay attention to the industry information channel to learn more related knowledge.

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