一句话：提出 RecRank，用 API 使用路径特征与排序式判别模型，在不依赖历史仓库的前提下提升 top-1 API 推荐准确率。
学习并定位完成编程任务所需的正确 API 既耗时又费力，因此自动 API 推荐具有实用价值。已有的基于图的统计模型能够较有效地给出 top-10 API 候选，但在准确推荐真正的 top-1 API 上表现不佳。为弥补这一缺陷，作者提出 RecRank：一种基于排序的判别式方法，利用从代码中抽取的 API 使用路径特征对候选 API 进行区分与重排序，并且不依赖历史软件仓库。作者在由 1385+8 个开源项目组成的大规模语料上进行实证评估，结果表明，与当前最先进的 API 推荐方法相比，RecRank 在 top-1 API 推荐准确率和平均倒数排名（MRR）上均取得显著提升。
It is time-consuming and labor-intensive to learn and locate the correct API for programming tasks. Thus, it is beneficial to perform API recommendation automatically. The graph-based statistical model has been shown to recommend top-10 API candidates effectively. It falls short, however, in accurately recommending an actual top-1 API. To address this weakness, we propose RecRank, an approach and tool that applies a novel ranking-based discriminative approach leveraging API usage path features to improve top-1 API recommendation. Empirical evaluation on a large corpus of (1385+8) open source projects shows that RecRank significantly improves top-1 API recommendation accuracy and mean reciprocal rank when compared to state-of-the-art API recommendation approaches.
DOI 原文 ·
@inproceedings{paperbot4725,
title = {Effective API recommendation without historical software repositories},
author = {Xiaoyu Liu and LiGuo Huang and Vincent Ng},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3238216}
}