How are general identifiers predicting e.g. functions from an available library? (besides the NL pointer net)

[brando90]

I was wondering in real programming we have usually a library of programs we have at our disposal. Generating new programs for each part sounds hopeless so I was wondering how tree gen solves this problem.

Is it that the tasks that tree gen was applied to has a small library it can choose from? (note I am not asking about the things in the NL that the pointer net is used for, I had more in mind like say we have the function zip or for a specific library say pandas we have a bunch of function the model could use etc how to choose those)

Is it that for GEO & HS the set of available function is always given and we could also just point to them with a pointer net?

I am trying to understand how much this question is a limitation of the model vs a limitation of the task.

Thanks again for all the useful responses! :)

[zysszy]

In our approach, we only collect identifiers from the training set rather than from a library.

Is it that the tasks that tree gen was applied to has a small library it can choose from?

Maybe generating java programs from the commit message (commit message generation dataset).

Is it that for GEO & HS the set of available function is always given and we could also just point to them with a pointer net?

HS dataset is collected from an open-source repository. Maybe you can find the functions from that repository.

Zeyu