To be included in this analysis, a language must be observable within both GitHub and Stack Overflow.With that description out of the way, please keep in mind the other usual caveats. For Stack Overflow, we simply collect the required metrics using their useful data explorer tool. 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 State of the Octoverse. The data source used for the GitHub portion of the analysis is the GitHub Archive. 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 specific 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. In the meantime, as a reminder, this work is a continuation of the work originally performed by Drew Conway and John Myles White late in 2010. We’ve been tied up behind the scenes at RedMonk between projects, our first event in three years and more and haven’t had the time to drop these yet, but the data’s been sitting and waiting for us. Now that we’re halfway through the fourth quarter, it’s probably about time for us to post our third quarter language rankings.
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