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.
@article{arxiv.1912.00609,
title = {GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach based on GAN},
author = {Yabing Zhu and Yanfeng Zhang and Huili Yang and Fangjing Wang},
journal= {arXiv preprint arXiv:1912.00609},
year = {2019}
}