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Consider the case where a programmer has written some part of a program, buthas left part of the program (such as a method or a function body) incomplete.The goal is to use the context surrounding the missing code to automatically'figure out' which of the codes in the database would be useful to theprogrammer in order to help complete the missing code. The search is'contextualized' in the sense that the search engine should use clues in thepartially-completed code to figure out which database code is most useful. Theuser should not be required to formulate an explicit query. We castcontextualized code search as a learning problem, where the goal is to learn adistribution function computing the likelihood that each database codecompletes the program, and propose a neural model for predicting which databasecode is likely to be most useful. Because it will be prohibitively expensive toapply a neural model to each code in a database of millions or billions ofcodes at search time, one of our key technical concerns is ensuring a speedysearch. We address this by learning a 'reverse encoder' that can be used toreduce the problem of evaluating each database code to computing a convolutionof two normal distributions.

Searching a Database of Source Codes Using Contextualized Code Search