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Fair points. And of course basing the github measure on lines of code gives quite an unfair advantage to verbose languages like Java. Perhaps they could look

by mjw 12y ago

Fair points. And of course basing the github measure on lines of code gives quite an unfair advantage to verbose languages like Java. Perhaps they could look at compression ratios when (say) gzipping a decent sample of each language, and use these to weight the LOC metric?

Sandman 12y ago
Or they could do away with LOC as a metric entirely and use number of repos instead?
- dtech 12y ago
  That would probably overvalue scripting languages, as they are more likely to be used for smaller languages. Also a lot of repos contain multiple languages (web languages more often than not do)
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 ago
  A 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).

by mjw 12y ago

Sandman 12y ago

Or they could do away with LOC as a metric entirely and use number of repos instead?

dtech 12y ago
That would probably overvalue scripting languages, as they are more likely to be used for smaller languages. Also a lot of repos contain multiple languages (web languages more often than not do)

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 ago
A 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).