An API Recommendation Method for Querying Mobile Computing Problems

Wanzhi Wen, Bin Zhang, Yitao Hu, Xingyu Zhu and Zelin Wang
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Wanzhi Wen: School of Information Science and Technology, Nantong University, China
Bin Zhang: School of Information Science and Technology, Nantong University, China
Yitao Hu: School of Information Science and Technology, Nantong University, China
Xingyu Zhu: School of Information Science and Technology, Nantong University, China
Zelin Wang: School of Information Science and Technology, Nantong University, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2024, vol. 18, issue 1, 1-16

Abstract: Nowadays mobile computing has penetrated into our lives and work. Major companies have invested a lot of material and financial resources in the development of mobile terminals. For developers, querying the right API is extremely important. However, finding the right APIs can be time-consuming and laborious. In this paper, to solve the problems developers may face in the actual development process and improve the development efficiency, we propose RAMC (Recommendation APIs for Mobile Computing), an API recommendation approach leveraging word embedding technique and the information crawling from Stack Overflow posts and Java core packages, to recommend appropriate APIs for developers. Furthermore, RAMC also provides developers with label words, similar questions and relevant code. To evaluate the effectiveness of RAMC, we decided to analyze our system by simulating an instance. By testing a problem encountered during mobile computing development, the RAMC can effectively output the much-related API and tags for developers.

Date: 2024
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