Machine Translation from Natural Language to Code using Long-Short Term Memory

TL;DR

This paper introduces a machine learning model using RNN and LSTM to translate natural language into programming code, achieving 74.40% accuracy, aiming to simplify programming for humans.

Contribution

It presents a novel approach combining RNN and LSTM for natural language to code translation, improving accessibility of programming.

Findings

Achieved 74.40% accuracy in translating natural language to code.

Proposed model can be enhanced with additional techniques.

Addresses the human-computer language barrier in programming.

Abstract

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.