API Recommendation Based on WII-WMD

Wanzhi Wen, Shiqiang Wang, Bingqing Ye, XingYu Zhu, Yitao Hu, Xiaohong Lu, Bin Zhang

Source Title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)15(4)

ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.20211001.oa16

MLA

Wen, Wanzhi, et al. "API Recommendation Based on WII-WMD." IJCINI vol.15, no.4 2021: pp.1-20. http://doi.org/10.4018/IJCINI.20211001.oa16

APA

Wen, W., Wang, S., Ye, B., Zhu, X., Hu, Y., Lu, X., & Zhang, B. (2021). API Recommendation Based on WII-WMD. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-20. http://doi.org/10.4018/IJCINI.20211001.oa16

Chicago

Wen, Wanzhi, et al. "API Recommendation Based on WII-WMD," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-20. http://doi.org/10.4018/IJCINI.20211001.oa16

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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.