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2019

Predicting Variable Types in Dynamically Typed Programming Languages

Jangda, Abhinav, Anand, Gaurav

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Dynamic Programming Languages are quite popular because they increase the programmer's productivity.

However, the absence of types in the source code makes the program written in these languages difficult to understand and virtual machines that execute these programs cannot produced optimized code.
To overcome this challenge, we develop a technique to predict types of all identifiers including variables, and function return types.
We propose the first implementation of $2^{nd}$ order Inside Outside Recursive Neural Networks with two variants (i) Child-Sum Tree-LSTMs and (ii) N-ary RNNs that can handle large number of tree branching.

Predicting Variable Types in Dynamically Typed Programming Languages · Around

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