GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach based on GAN

TL;DR

GANCoder is an automatic natural language-to-programming language translation method utilizing GANs, which improves code generation quality and stability by adversarial training, achieving comparable accuracy to state-of-the-art approaches.

Contribution

This paper introduces GANCoder, the first GAN-based approach for natural language to programming language translation, enhancing stability and quality of generated code.

Findings

Achieves comparable accuracy with state-of-the-art methods

Demonstrates improved stability in code generation

Effective adversarial training enhances code quality

Abstract

We propose GANCoder, an automatic programming approach based on Generative Adversarial Networks (GAN), which can generate the same functional and logical programming language codes conditioned on the given natural language utterances. The adversarial training between generator and discriminator helps generator learn distribution of dataset and improve code generation quality. Our experimental results show that GANCoder can achieve comparable accuracy with the state-of-the-art methods and is more stable when programming languages.