CASE: A Platform for Crowdsourcing Based API Search

Tingting Liang, Liang Chen, Zhining Xie, Wei Yang, Jian Wu

Abstract: With the rapid growth of Web APIs on the Internet, searching appropriate APIs is becoming a challenging problem. General API search systems (e.g., ProgrammableWeb) implement API search through simple keywords matching leading to unsatisfactory search results. In this paper, we presents a crowdsourcing based API search engine CASE. Specifically, the API search engine leverages social information, Twitter List, a tool used by individual users to organize accounts that interest them on semantics. Based on the lists information, Latent Semantic Indexing (LSI) model is employed to compute the semantic similarity between the APIs and queries. Furthermore, the popularity of APIs inferred from the lists number is integrated with the semantic similarity to generate the final search result.