一句话：提出 SAR，在少量知识条件下学习跨语言 API 映射，以降低程序翻译对大规模平行语料的依赖。
在跨语言程序翻译中，开发者需要将一种语言使用的 API 替换为另一种语言中功能等价的 API，因此自动识别跨语言 API 映射十分关键。现有方法虽然能辅助这种翻译，但通常依赖大量平行语料，包括功能等价的 API 对、代码片段或相似代码注释，构建成本很高。该论文提出 SAR，目标是在仅有少量知识的条件下学习跨语言 API 映射，从而减少对大规模平行语料的依赖。不过，给出的摘要仅说明了问题与动机，未具体披露 SAR 的方法细节、实验设置与关键结果。
To save effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, these approaches still require large amount of parallel corpora, ranging from pairs of APIs or code fragments that are functionally equivalent, to similar code comments.
DOI 原文 ·
@inproceedings{paperbot5069,
title = {SAR: learning cross-language API mappings with little knowledge},
author = {Nghi D. Q. Bui and Yijun Yu and Lingxiao Jiang},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3338924}
}