Generating similarities for code generation
Got it, so maybe a ‘hybrid’ approach? I.e. encode the code snippets as class/function/interface name + parameter_names + docstrings as a ‘syntactic’ embedding, and then use a code2seq or the like to generate embeddings based on their AST paths (and get the ‘semantic’ meaning as well). Then whatever the user prompts, I can generate an embedding based off of his prompt (whether a textual description or code) and see if I get some good similarity results for relevant coding snippets. Does this make...
https://community.openai.com/t/generating-similarities-for-code-generation/276894/9