An alternative approach from GPT-3 Copilot to improve developer productivity
To be honest, I’ve always been sceptical about AI-based autocomplete. When I program, I’m deeply focused on a problem and I have my preferences on how to write code. Suggestions that pop up here and there are a huge distraction for me. That’s why tools like Kite or Tab Nine have never worked for me. Copilot, a dev productivity tool from GitHub and OpenAI however, has different user interfaces that are more accommodating to a user. But…
Natural language models like GPT-3 are good for language related tasks, but for programming use cases they leave a lot of valuable information on the table, e.g. grammatical information that we can get from static analysis, compilation, etc. By applying transformations on a syntax tree (ST) level, we can use programming language grammar to modify a codebase depending on our use case. I think this alternative approach that doesn’t require character by character or line by line generation is very promising. This is the approach that I truly believe in.
Working with the fundamental level of the grammar of programming languages, we can develop a universal algorithm, and, more importantly, with this approach, it’s more scalable to all programming languages because the grammar of under-the-hood programming languages has a similar structure.
