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You are absolutely right, I didn't think about this when I wrote my comment. LOC is in itself a very poor metric, therefore "Changed LOC" doesn't improve any i
by cessor 12y ago
You are absolutely right, I didn't think about this when I wrote my comment. LOC is in itself a very poor metric, therefore "Changed LOC" doesn't improve any interpretation. Including the number and sizes of repos would be better, since LOC pushes verbose languages. Java and C# code usually features a lot of empty or hollow lines (in C# for example, one opens the curly brace for a function in a new line).
I mean, yeah, most of the comments indicate that we agree that this kind of graph or "false statistic" is flawed.
But can we find any value in it? How do we interpret this graph, despite its basic problems? What good stuff can we do with it?
- mjw 12y agoA scale for cross-language comparisons seems a hard ask because everyone is implicitly interested in answering different questions. Which languages do people enjoy using the most? Which have the most code "out there" in some setting or other (open source, commerce, in deployment, scripting, ...)? Which have the most engaged and vocal communities? Which have the biggest pool of skilled workers? which will I get the most career benefit from learning? Perhaps the useful information is more in the correlations between different related metrics, than in drawing up ranked lists (surprise surprise, Java > Haskell!). This graph helps visualise the correlation between these two, which seems significant but far from perfect. Outliers like SQL can then be identified, which point to problems with the metrics (e.g. with SQL, presumably github fails to spot lines of SQL embedded in other langauges).