一句话：针对自动 API 推荐中查询序列语义丢失与词汇相似查询难区分的问题，论文提出 CLEAR。
自动 API 推荐已有信息检索（IR）与神经网络两条技术路线，尽管报告了良好性能，作者观察到它们仍会因两个原因失败：一是多数 IR 方法将任务查询当作词袋并用词嵌入表示，无法捕捉查询的序列语义；二是 IR 方法与神经方法都难以区分词汇相似查询之间的语义差异。论文 CLEAR 针对这些问题展开研究，因此需在保留序列语义的同时增强相似查询的语义区分；但给定摘要未披露具体方法、数据集与评估结果。
Automatic API recommendation has been studied for years. There are two orthogonal lines of approaches for this task, i.e., information-retrieval-based (IR-based) and neural-based methods. Although these approaches were reported having remarkable performance, our observation shows that existing approaches can fail due to the following two reasons: 1) most IR-based approaches treat task queries as bag-of-words and use word embedding to represent queries, which cannot capture the sequential semantic information. 2) both the IR-based and the neural-based approaches are weak at distinguishing the semantic difference among lexically similar queries.
DOI 原文 ·
@inproceedings{paperbot6926,
title = {CLEAR},
author = {Moshi Wei and Nima Shiri Harzevili and Yuchao Huang and Junjie Wang and Song Wang},
booktitle = {ICSE},
year = {2022},
doi = {10.1145/3510003.3510159}
}