Improving identifier informativeness using part of speech information
Improving identifier informativeness using part of speech information
复制标题
DOI：
10.1145/1985441.1985471
复制
发表时间：
2011-05
期刊：
影响因子：
--
通讯作者：
D. Binkley;Matthew Hearn;Dawn J Lawrie
中科院分区：
文献类型：
--
作者：
D. Binkley;Matthew Hearn;Dawn J Lawrie
最近的软件开发工具利用了对软件及其支持文档中发现的自然语言信息的挖掘。为了充分利用这些信息，研究人员借鉴了自然语言处理社区的工具和技术。其中一种工具提供词性信息，可用于改进软件存储库的搜索和提取标识符中的域信息。不幸的是，在软件中发现的自然语言与在标准散文中发现的自然语言不同。这种差异可能会限制现成工具的有效性。一项实证调查发现，在极少的指导下，现有的标注器在标记源代码标识符中的单词时的正确率为88%。然后，调查使用改进的词性信息来标记超过145,000个结构字段名称的大型语料库。从标签中的模式中出现了一些规则，它们试图理解过去的用法并改进将来的命名。
Recent software development tools have exploited the mining of natural language information found within software and its supporting documentation. To make the most of this information, researchers have drawn upon the work of the natural language processing community for tools and techniques. One such tool provides part-of-speech information, which finds application in improving the searching of software repositories and extracting domain information found in identifiers. Unfortunately, the natural language found is software differs from that found in standard prose. This difference potentially limits the effectiveness of off-the-shelf tools. An empirical investigation finds that with minimal guidance an existing tagger was correct 88% of the time when tagging the words found in source code identifiers. The investigation then uses the improved part-of-speech information to tag a large corpus of over 145,000 structure-field names. From patterns in the tags several rules emerge that seek to understand past usage and to improve future naming.