GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach based on GAN
This addresses the challenge of code generation from natural language for developers, but it is incremental as it builds on existing GAN and translation methods.
The authors tackled the problem of automatically translating natural language to programming language code using a GAN-based approach, achieving comparable accuracy to state-of-the-art methods with improved stability.
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.