Multi-Modal API Recommendation

Too many options can be a problem, which is the case for Application Programming Interfaces (APIs). As there are many such APIs, with many more being introduced periodically, it raises the problem of choosing which API to be recommended. Furthermore, numerous APIs are commonly used together with other complementary third-party APIs. It can be challenging for developers to understand how to use each API and to remember all the complementary APIs for the API they want to use. Therefore, an accurate API recommendation approach can improve developers' efficiency in implementing certain functionality. Several approaches have been developed to automatically recommend APIs based on either a natural language query or source code context. However, none of these API recommendation approaches have utilized these two sources of information at the same time (i.e., leveraging natural language query and source code context together). In this work, we propose an approach named MuLAREc, which leverages the information from natural language query (annotation) and source code context. The results confirm that our approach outperforms state-of-the-art API recommendation approaches which only leverage a single type of information as the input. Our work also demonstrates that multi-modal information can boost the performance of API recommendation approaches by 20%-50% better in terms of BLEU-score than the baselines.