CLEAR: Contrastive Learning for API Recommendation

Moshi Wei, Nima Shiri Harzevili, Yuchao Huang, Junjie Wang, Song Wang

Abstract

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.

BibTeX

@inproceedings{Wei-al:ICSE22,
  author    = {Moshi Wei and
               Nima Shiri Harzevili and
               Yuchao Huang and
               Junjie Wang and
               Song Wang},
  title     = {{CLEAR:} Contrastive Learning for {API} Recommendation},
  booktitle = {ICSE},
  pages     = {376--387},
  publisher = {{ACM}},
  year      = {2022},
}

CLEAR: Contrastive Learning for API Recommendation

Abstract

Related papers