MULAPI: Improving API method recommendation with API usage location

Abstract During the evolution of a software system, a large number of feature requests are continuously proposed by users. To implement these feature requests, developers often utilize existing third-party libraries and make use of Application Programming Interfaces (APIs) to accelerate the feature implementation process. However, it is not always obvious which API methods are suitable and where these API methods can be used in the target program. In this paper, we propose an approach, MULAPI (Method Usage and Location for API), to recommend API methods and figure out the API usage location where these API methods would be used. MULAPI employs feature location to identify feature related files as API usage location. Further, these feature related files are taken into account to recommend API methods by exploring the source code repository and API libraries as well. We evaluate MULAPI on more than 1000 feature requests of eight Java projects (Axis/Java, CXF, Hadoop Common, Hbase, Struts2, Hadoop HDFS, Hive and Hadoop Map/Reduce), and recommend API methods from ten third-party libraries. The empirical results show that MULAPI can accurately recommend API methods and usage location, and moreover, MULAPI improves the effectiveness of API method recommendation, compared with the state-of-the-art approach.