An Ensemble Approach for Annotating Source Code Identifiers With Part-of-Speech Tags
An Ensemble Approach for Annotating Source Code Identifiers With Part-of-Speech Tags
复制标题
用词性标签注释源代码标识符的集成方法
DOI：
10.1109/tse.2021.3098242
复制
发表时间：
2021
影响因子：
7.4
通讯作者：
Emily Hill
中科院分区：
文献类型：
--
作者：
Christian D. Newman;M. J. Decker;Reem S. Alsuhaibani;Anthony S Peruma;Satyajit Mohapatra;Tejal Vishnoi;Marcos Zampieri;Mohamed Wiem Mkaouer;T. J. Sheldon;Emily Hill
本文提出了一种用于源代码标识符的集成词性标注方法。集成标注是一种利用机器学习和多个词性标注器的输出来标注自然语言文本的技术，其质量比词性标注器独立获得的质量更高。我们的集合使用了三个最先进的词性标注器：SWUM、POSSE和Stanford。我们在单个单词和完整标识符名称的级别上研究了集成对五种不同类型标识符名称的注释的质量：函数、类、属性、参数和声明语句。我们还研究和讨论了我们的标记器的弱点，以促进未来通过进一步的研究改进这些问题。我们的结果表明，该集成在标识符级别上达到75%的准确率，在单词级别上达到84- 86%的准确率。这比最接近的独立词性标注器在标识符水平上增加了+17%。
This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble’s annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75 percent accuracy at the identifier level and 84-86 percent accuracy at the word level. This is an increase of +17% points at the identifier level from the closest independent part-of-speech tagger.
DOI：
10.1109/icsme.2019.00040
发表时间：
2019-09
期刊：
2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)
影响因子：
--
作者：
Christian D. Newman;M. J. Decker;Reem S. Alsuhaibani;Anthony S Peruma;D. Kaushik;Emily Hill
通讯作者：
Christian D. Newman;M. J. Decker;Reem S. Alsuhaibani;Anthony S Peruma;D. Kaushik;Emily Hill
DOI：
10.1109/icpc52881.2021.00039
发表时间：
2021-03
期刊：
2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)
影响因子：
--
作者：
Anthony S Peruma;Emily Hu;Jiajun Chen;E. Alomar;Mohamed Wiem Mkaouer;Christian D. Newman
通讯作者：
Anthony S Peruma;Emily Hu;Jiajun Chen;E. Alomar;Mohamed Wiem Mkaouer;Christian D. Newman
DOI：
10.1016/j.jss.2020.110704
发表时间：
2020-11
期刊：
J. Syst. Softw.
影响因子：
--
作者：
Anthony S Peruma;Mohamed Wiem Mkaouer;M. J. Decker;Christian D. Newman
通讯作者：
Anthony S Peruma;Mohamed Wiem Mkaouer;M. J. Decker;Christian D. Newman