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Source code usage via n-gram analysis

In the last 18 months or so source code analysis using n-grams has started to take off as a research topic, with most of the current research centered around lexical tokens as the base unit. The usefulness of n-grams for getting an idea of the main kinds of patterns that occur in code is one of those techniques that has been long been spread via word of mouth in industry (I used to use it a lot to find common patterns in generated code, with the intent of optimizing that common pattern).

At the general token level n-grams can be used to suggest code completion sequences in IDEs and syntax error recovery tokens for compilers (I have never seen this idea implemented in a production compiler). Token n-grams are often very context specific, for instance the logical binary operators are common in control-expressions (e.g., in an if-statement) and rare outside of that context. Of course any context can be handled if the n in n-gram is very large, but every increase in n requires a lot more code to be counted, to separate out common features from the noise of many single instances ((n+1)-grams require roughly an order of T more tokens than n-gram, where T is the number of unique tokens, for the same signal/noise ratio).

At a higher conceptual level n-grams of language components (e.g, array/member accesses, expressions and function calls) provide other kinds of insights into patterns of code usage. Probably more than you want to know of this kind of stuff in table/graphical form and some Java data for those wanting to wave their own stick.

An n-gram analysis sometimes involves a subset of the possible tokens, for instance treating sequences of function/method api calls that does not match the any of the common sequences as possible faults (e.g., a call that should have been made, perhaps closing a file, was omitted).

It is easy to spot n-gram researchers who have no real ideas of their own and are jumping on the bandwagon; they spend most of their time talking about entropy.

Like all research topics, suggested uses for n-grams sometimes gets rather carried away with its own importance. For instance, the proposal that common code sequences are “natural” and have a style that must be followed by other code. Common code sequences are common for a reason and we ought to find out what that reason is before suggesting that developers blindly mimic this usage.

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