We use the aggregated history to determine ranking (though based on the table structure changes this can no longer be accomplished via a single query.).While this continues to have the caveats outlined below, it does have the benefit of cohesion with our previous methodology. Language is based on the base repository language.Our query is designed to be as comparable as possible to the previous process. We query languages by pull request in a manner similar to the one GitHub used to assemble the 2016 State of the Octoverse. The data source used for these queries is the GitHub Archive. In the meantime, here’s how the rankings are performed currently. For more on these changes and the history of our rankings, see the summary from our last run. In January 2014 and again in January 2017, we were forced to make a change to the way that GitHub’s rankings were collected due to changes in the data’s availability. The idea is not to offer a statistically valid representation of current usage, but rather to correlate language discussion and usage in an effort to extract insights into potential future adoption trends. While the means of collection has changed, the basic process remains the same: we extract language rankings from GitHub and Stack Overflow, and combine them for a ranking that attempts to reflect both code (GitHub) and discussion (Stack Overflow) traction. As always, these are a continuation of the work originally performed by Drew Conway and John Myles White late in 2010. Given that we’re into March, it seems like a reasonable time to publish our Q1 Programming Language Rankings. Experts in custom software development and pioneers in cutting edge technologies (Node, React) we enable you to innovate and stay ahead of the competition. This iteration of the RedMonk Programming Language Rankings is brought to you by YLD.
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