Review on Code Generator with statistical Models

ABSTRACT

We present a new code assistance tool for integrated development environments. Our system accepts as input free-form queries containing a mixture of English and Java, and produces python code expressions that take the query into account and respect syntax, types, and scoping rules of Java, python as well as statistical usage patterns. In contrast to solutions based on code search, the results returned by our tool need not directly correspond to any previously seen code fragment. The encoder learns a vector representation of the pseudo code snippet which the decoder then takes as input to learn the code output. Because of the separate forget and input gates, LSTMs have plenty of versatility for both long and short input sequences and prove a strong framework for this task. In this paper, various techniques are discussed to  customized natural language processing tool chain to extract information from free-form text queries.

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