  Application Programming Interfaces (APIs) are designed to help developers
build software more effectively. Recommending the right APIs for specific tasks
has gained increasing attention among researchers and developers in recent
years. To comprehensively understand this research domain, we have surveyed to
analyze API recommendation studies published in the last 10 years. Our study
begins with an overview of the structure of API recommendation tools.
Subsequently, we systematically analyze prior research and pose four key
research questions. For RQ1, we examine the volume of published papers and the
venues in which these papers appear within the API recommendation field. In
RQ2, we categorize and summarize the prevalent data sources and collection
methods employed in API recommendation research. In RQ3, we explore the types
of data and common data representations utilized by API recommendation
approaches. We also investigate the typical data extraction procedures and
collection approaches employed by the existing approaches. RQ4 delves into the
modeling techniques employed by API recommendation approaches, encompassing
both statistical and deep learning models. Additionally, we compile an overview
of the prevalent ranking strategies and evaluation metrics used for assessing
API recommendation tools. Drawing from our survey findings, we identify current
challenges in API recommendation research that warrant further exploration,
along with potential avenues for future research.
