  Making computer programming language more understandable and easy for the
human is a longstanding problem. From assembly language to present day's
object-oriented programming, concepts came to make programming easier so that a
programmer can focus on the logic and the architecture rather than the code and
language itself. To go a step further in this journey of removing
human-computer language barrier, this paper proposes machine learning approach
using Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) to
convert human language into programming language code. The programmer will
write expressions for codes in layman's language, and the machine learning
model will translate it to the targeted programming language. The proposed
approach yields result with 74.40% accuracy. This can be further improved by
incorporating additional techniques, which are also discussed in this paper.

Computation and Language;Artificial Intelligence;Programming Languages