A Comparison of Code Embeddings and Beyond

Understand

Program representation learning is a fundamental task in software engineering applications.

• With the availability of "big code" and the development of deep learning techniques, various program representation learning models have been proposed to understand the semantic properties of programs and applied on different software engineering tasks.
• However, no previous study has comprehensively assessed the generalizability of these deep models on different tasks, so that the pros and cons of the models are unclear.
• In this experience paper, we try to bridge this gap by systemically evaluating the performance of eight program representation learning models on three common tasks, where six models are based on abstract syntax trees and two models are based on plain text of source code.