Crowdsourcing Based API Search via Leveraging Twitter Lists Information

With the rapid growth of open APIs on the Internet, searching appropriate APIs for a given query becomes a challenging problem. General API search systems, such as ProgrammableWeb, usually can not provide satisfactory results of API search due to the simple keywords matching between queries and API information offered by providers (e.g. name and description). In this paper, we propose a crowdsourcing based search approach named CrowdAPS to effectively find the appropriate APIs. Specifically, CrowdAPS leverages Twitter lists, which is a tool used by individual users to organize accounts that interest them on semantics. List meta-data, including list name and description, is generated from collective intelligence and can be used by Latent Semantic Indexing (LSI) model to acquire semantic similarity between APIs and queries. Furthermore, CrowdAPS exploits list number to infer the popularity of APIs. The final search result relies on the integration of semantic similarity and popularity. Comprehensive experiment based on real-world datasets crawled from ProgrammableWeb and Twitter demonstrates the effectiveness of CrowdAPS.