How to solve the problem of variable-sized AST as input for a...

mike_plumbs

In my work I have a given source code for a module. From this module I generate an AST, whose size is dependent on the size of the module (e.g. more source code -> bigger AST). I want to train a neural network model which will learn a general structure of a module and be able to rate (on a scale of 0 to 1) how "good" a module is structure wise (if requires are at the beginning, followed by local functions, variables and finally returns). Now I...

Originally asked by Michael Kročka on Stack Exchange (ai) (CC BY-SA 4.0).

uphilltech

Great question — answering as the UphillTech team:

You discovered already one solution for your problem: Zero-Padding. There are two other common possibilities: Using Recurrent NNs This is often used at text processing, where you feed each word one after another into your model. Using Recursive NNs (I wont recommend this for your use case) This method is also frequently used in word processing, but is more often applied in the semantic analysis of text. You reduce the text to the essential, until it has reached the desired length. However, information is lost in this process.

Full answer by Luca Jung: https://ai.stackexchange.com/a/18057 (CC BY-SA 4.0).

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