Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding
This work addresses a domain-specific problem for software developers by improving tools for API analysis and recommendation, though it is incremental as it builds on existing embedding techniques.
The paper tackles the problem of semantic gaps between natural language words and APIs by proposing Word2API, which simultaneously models words and APIs using a shuffling strategy and large-scale data from code repositories. The method outperforms baselines by 10%-49.6% in relatedness estimation and improves API recommendation and document linking tasks by up to 21.4% and 7.9%-17.4%, respectively.
Developers increasingly rely on text matching tools to analyze the relation between natural language words and APIs. However, semantic gaps, namely textual mismatches between words and APIs, negatively affect these tools. Previous studies have transformed words or APIs into low-dimensional vectors for matching; however, inaccurate results were obtained due to the failure of modeling words and APIs simultaneously. To resolve this problem, two main challenges are to be addressed: the acquisition of massive words and APIs for mining and the alignment of words and APIs for modeling. Therefore, this study proposes Word2API to effectively estimate relatedness of words and APIs. Word2API collects millions of commonly used words and APIs from code repositories to address the acquisition challenge. Then, a shuffling strategy is used to transform related words and APIs into tuples to address the alignment challenge. Using these tuples, Word2API models words and APIs simultaneously. Word2API outperforms baselines by 10%-49.6% of relatedness estimation in terms of precision and NDCG. Word2API is also effective on solving typical software tasks, e.g., query expansion and API documents linking. A simple system with Word2API-expanded queries recommends up to 21.4% more related APIs for developers. Meanwhile, Word2API improves comparison algorithms by 7.9%-17.4% in linking questions in Question&Answer communities to API documents.