Bibliographic Analysis for Code search & API Recommendation Literatures

Auhors: Liming Nie, He Jiang, Guojun Gao, Han Wang, Xiujuan Xu.

Abstract: Code search and API recommendation algorithms can help developers to effectively implement programming tasks. Up to now, the researchers publish a series of related literatures. Although some scholars have elaborated the background and research status of the research field, the researchers have not yet understood some basic knowledge in this field, such as the most productive author, institution and country, the most influential author and literature, as well as popular research hotspots, and so on.

With the help of a classical bibliographic analysis framework, in this paper, we firstly carry out a basic literature analysis and an exploration of several networks on the basis of literature data in this research field. On the one hand, the results of the basic literature analysis show that in recent years, more and more researchers are paying attention to the researches in this field. The most productive author is Cristina Videira Lopes, University of California at Irvine is the institution with the most published papers, most of the literatures are from the United States, and the most influential author is Denys Poshyvanyk. On the other hand, the results of the exploration for three networks show that Tao Xie, Cristina Videira Lopes and Denys Poshyvany are the three most active authors in the field, and the performance improvement of the algorithms and their applications in software engineering tasks are the most popular research topics.

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