CLEAR: Contrastive Learning for API Recommendation

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. In this paper, we propose CLEAR, which leverages BERT sen-tence embedding and contrastive learning to tackle the above two is-sues. Specifically, CLEAR embeds the whole sentence of queries and Stack Overflow (SO) posts with a BERT-based model rather than the bag-of-word-based word embedding model, which can preserve the semantic-related sequential information. In addition, CLEAR uses contrastive learning to train the BERT-based embedding model for learning precise semantic representation of programming termi-nologies regardless of their lexical information. CLEAR also builds a BERT-based re-ranking model to optimize its recommendation results. Given a query, CLEAR first selects a set of candidate SO posts via the BERT sentence embedding-based similarity to reduce search space. CLEAR further leverages a BERT-based re-ranking model to rank candidate SO posts and recommends the APIs from the ranked top SO posts for the query. Our experiment results on three different test datasets confirm the effectiveness of CLEAR for both method-level and class-level API recommendation. Compared to the state-of-the-art API recom-mendation approaches, CLEAR improves the MAP by 25%-187% at method-level and 10%-100% at class-level.