Abstract
Neural machine translation (NMT) methods developed for natural language processing have been shown to be highly successful in automating translation from one natural language to another. Recently, these 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. In the literature, a variety of different input scenarios have been explored, including generating code based on natural language description, lower-level representations such as binary or assembly (neural decompilation), partial representations of source code (code completion and repair), and source code in another language (code translation). In this paper we survey the NMT for code generation literature, cataloging the variety of methods that have been explored according to input and output representations, model architectures, optimization techniques used, data sets, and evaluation methods. We discuss the limitations of existing methods and future research directions
Abstract (translated)
神经网络机器翻译(NMT)方法，是为自然语言处理而开发的，已经被证明能够在自动化从一种自然语言到另一种自然语言的转换方面取得成功。最近，这些NMT方法已经适应到了生成程序代码的 generation 任务。在 NMT 代码生成任务中，任务是生成满足输入所表达的限制的输出源代码。在文献中，已经探索了多种不同的输入场景，包括基于自然语言描述的生成代码、低层次的表示，如二进制或汇编(神经破解)、源代码的部分表示(代码 completion 和修复)以及用另一种语言的源代码(代码翻译)。在本文中，我们综述了 NMT 代码生成文献，并对输入和输出表示、模型架构、优化技术、数据集和评估方法等方面所探索的方法进行分类和总结。我们讨论了现有方法的局限性和未来研究的方向。
URL
https://arxiv.org/abs/2305.13504
