代码搜索与API推荐文献分析

doi:10.11896/j.issn.1002-137X.2017.6A.106

摘要/Abstract

摘要： 代码搜索和API推荐算法能够帮助开发者有效实现编程任务。截至目前,研究者们发表了一系列相关文献。尽管一些学者对该研究领域的背景和研究现状进行了阐述,但是研究者对该领域中的一些基本领域知识还缺乏了解,如最高产的作者、机构和国家,影响力较大的作者和文献,以及流行的热点研究等。借助经典的文献分析框架,在构建该研究领域文献数据仓库的基础上,首次对该领域的研究进行了基础文献分析和合作模式探索。一方面,基础文献分析的结果表明,近几年越来越多的研究者开始关注该领域的研究,最高产的作者是Cristina Videira Lopes,University of California at Irvine是发表相关文献最多的机构,大部分文献来自美国,根据领域H因子计算得到的最有影响力的作者是Denys Poshyvanyk。另一方面,合作模式的分析结果显示,Tao Xie,Cristina Videira Lopes 和 Denys Poshyvany是该领域最活跃的三位作者,推荐算法性能的提升及其在软件工程任务中的应用是目前该领域最流行的研究主题。

关键词: 文献分析,代码搜索,API推荐,合作,研究热点

Abstract: Code/API recommendation approaches can assist developers to implement programming tasks efficiently.Until now,lots of related literature are published.Although some researchers present the background and the state of this research field,there is a lack of understanding of some essential domain knowledge,such as the most productive author,institution,and country,the most popular literature and author,and the popular research hotspots.By employing a classical bibliographic analysis framework,we conducted a basic bibliographic analysis and explored cooperation among authors based on literature data.The analysis results show that,on the one hand,in the basic bibliographic analysis,we fond that the most productive author is Cristina Videira Lopes.The University of California at Irvine is the most productive institution,most of literature are from the USA,and the most influential author is Denys Poshyvanyk.On the other hand,in the research about cooperation among authors,we fond that three authors,i.e.,Tao Xie,Cristina Videira Lopes,and Denys Poshyvany are the most active authors in this research field.Two topics,i.e.,the performance improvement for recommendation algorithms and their applications on other software engineering tasks,are the most popular research topics.

Key words: Bibliographic analysis,Code search,API recommendation,Cooperation,Research interests

聂黎明,江贺,高国军,王涵,徐秀娟. 代码搜索与API推荐文献分析[J]. 计算机科学, 2017, 44(Z6): 475-482. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.106

NIE Li-ming, JIANG He, GAO Guo-jun, WANG Han and XU Xiu-juan. Bibliographic Analysis for Code/API Recommendation Literatures[J]. Computer Science, 2017, 44(Z6): 475-482. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.106

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