Dharma_kc Neural Machine Translation for Code Generation 2023

[TOC]

1. Title: Neural Machine Translation for Code Generation
2. Author: Dharma KC et. al.
3. Publish Year: 22 May 2023
4. Review Date: Sun, May 28, 2023
5. url: https://arxiv.org/pdf/2305.13504.pdf

Summary of paper

Motivation

• Recently, NMT methods have been adapted to the generation of program code. In NMT for code generation, the task is to generate output source code that satisfies constraints expressed in the input.

Conclusion

• NMT-based architecture are getting quite popular for source generation from various input. The NMT-based code generation is useful in multiple domains such as code generation from input binary or assembly (decompilation), code-to-code translation, code repair, bug fixing, and many more.
• some open problems
  • source code has long dependencies in multiple places
    • next-token prediction technique may lost the dependency information
  • Methods that can break down a problem into small problems, generate code for such subprograms, and evaluate them are good potential research direction
  • sample efficiency
  • Current code generation does not combine code abstraction to higher-level abstractions as human do.
  • Execution-guided synthesis currently works with DSLs, but extending them to real-world source code generation is a research direction.
  • Retrieve-and-Edit framework