一句话：BRAID 是一个 VSCode 插件式 API 推荐工具，利用隐式用户反馈并结合 learning-to-rank 与主动学习提升推荐效果。
高效的应用编程接口（API）推荐是现代集成开发环境最受期待的功能之一，已有大量推荐方法被提出，但多数现有推荐器无法将用户反馈有效融入推荐循环。本文提出 BRAID（Boosting RecommendAtion with Implicit FeeDback），一个利用用户反馈、并采用 learning-to-rank 与主动学习技术来提升推荐性能的工具。其实现基于 VSCode 插件架构，提供一体化的用户界面。本质上，BRAID 是一个通用框架，可将已有的基于查询的 API 推荐方法作为组件纳入其中。作者与强基线进行了对比实验，验证了该工具的有效性，并提供了演示视频展示其使用方式。
Efficient application programming interface (API) recommendation is one of the most desired features of modern integrated development environments. A multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we present BRAID (Boosting RecommendAtion with Implicit FeeDback), a tool which leverages user feedback, and employs learning-to-rank and active learning techniques to boost recommendation performance. The implementation is based on the VSCode plugin architecture, which provides an integrated user interface. Essentially, BRAID is a general framework which can accommodate existing query-based API recommendation approaches as components. Comparative experiments with strong baselines demonstrate the efficacy of the tool. A video demonstrating the usage of BRAID can be found at https://youtu.be/naD0guvl8sE.
DOI 原文 ·
@inproceedings{paperbot6459,
title = {BRAID: an API recommender supporting implicit user feedback},
author = {Yu Zhou and Haonan Jin and Xinying Yang and Taolue Chen and Krishna Narasimhan and Harald C. Gall},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473111}
}