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Abstract
- Learning to code in a new language takes a lot of time and effort for new as well as seasoned developers. There is a certain language that machines understand and we humans have to learn that language in order for us to communicate with the machine and make it do the things that we desire. To reduce this human-machine barrier of communication researchers have come up with a machine learning solution to translate natural language into code that allows developers to spend more time on logic and architecture rather than the actual instructions given to the machine. This paper surveys the approaches developed by various research scholars to translate natural language into machine understandable code. We analyze these approaches and state their boons and banes so that there is scope for improvement in this domain. This paper aims to give an idea about the various approaches in this domain to budding researchers wanting to contribute in this field.
Published Version
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