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Human vs automatically generated source code: an arms race?

How well do I understand the process of writing source, as performed by human developers? If I could write a program that could generate source code that was indistinguishable from human written code, then I think I could claim to have a decent understanding of the human processes.

An important question is the skill of the entity attempting to distinguish automatically generated and human written code. As of today I think I could fool existing tools (mostly because such tools don’t really exist; n-gram analysis is really all there is), but a human developer with a smattering of coding experience would not be fooled.

I think I know enough to write a tool that could generate single function definitions that would fool most developers, but not a moderately sophisticated analysis tool (I don’t know enough to combine multiple functions into a plausible call graph).

At the CREST workshop today I met Martin Monperrus who shared my view that the only way to prove an understanding of source is to be able to generate it; other people at the workshop seemed more interested in the detection side of things.

Perhaps the time is ripe to kick off a generator/detector arms race would certainly increase our knowledge of the characteristics of human written code.

What counts as automatically generated code? Printing complete function definitions mined from Github is obviously not what most people would associate with the concept of automatic source generation. What about printing statements mined from Github (with each statement coming from a different function definition)? Well, if people could make that work, then good luck to them (it hardly seems worth the bother because most statements are very simple and easily generated on a token by token basis).

Should functions be judged in isolation, or should a detector get to see multiple instances of each case (which would make the detector’s life easier)? It might be best to go with whatever rules make for an interesting arms race.

To start the ball rolling, here is my first entry to the generator side of the race (a rather minimal entry, but still the leader in its field; the recurrent neural network trained on the Linux source would easily beat it, but my interest is in understanding developer behavior and will leave it to others to go down the neural network path).

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