一句话：提出上下文感知的代码到代码推荐工具 Lancer，结合 LSLM 与 BERT 实时推荐可复用代码。
编程通常困难且重复，程序员需要反复编写相似代码并解决无穷无尽的问题。为避免重复造轮子并提升生产力，作者提出上下文感知的代码到代码推荐工具 Lancer。该工具在库敏感语言模型（Library-Sensitive Language Model, LSLM）与 BERT 模型的支持下，能够自动分析不完整代码的意图，并实时推荐相关且可复用的代码样本。摘要中给出了 Lancer 的视频演示链接，并说明该工具已开源。摘要未报告定量实验结果或用户研究结果，因此其关键结果主要体现在工具功能演示与开源实现上。
Programming is typically a difficult and repetitive task. Programmers encounter endless problems during programming, and they often need to write similar code over and over again. To prevent programmers from reinventing wheels thus increase their productivity, we propose a context-aware code-to-code recommendation tool named Lancer. With the support of a Library-Sensitive Language Model (LSLM) and the BERT model, Lancer is able to automatically analyze the intention of the incomplete code and recommend relevant and reusable code samples in real-time. A video demonstration of Lancer can be found at https://youtu.be/tO9nhqZY35g. Lancer is open source and the code is available at https://github.com/sfzhou5678/Lancer.
DOI 原文 ·
@inproceedings{paperbot5192,
title = {Lancer: Your Code Tell Me What You Need},
author = {Shufan Zhou and Beijun Shen and Hao Zhong},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00137}
}