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.
CITATION STYLE
Zhu, Y., Zhang, Y., Yang, H., & Wang, F. (2019). GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach Based on GAN. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11839 LNAI, pp. 529–539). Springer. https://doi.org/10.1007/978-3-030-32236-6_48
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