API Recommendation Based on WII-WMD

Wanzhi Wen, Shiqiang Wang, Bingqing Ye, XingYu Zhu, Yitao Hu, Xiaohong Lu and Bin Zhang
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Wanzhi Wen: Nantong University, China
Shiqiang Wang: Nantong University, China
Bingqing Ye: Nantong University, China
XingYu Zhu: Nantong University, China
Yitao Hu: Nantong University, China
Xiaohong Lu: Nantong University, China
Bin Zhang: Nantong University, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2021, vol. 15, issue 4, 1-20

Abstract: Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.

Date: 2021
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