Statistical Unigram Analysis for Source Code Repository
| dc.contributor.author | Xu, Weifeng | |
| dc.contributor.author | Xu, Dianxiang | |
| dc.contributor.author | Ariss, Omar El | |
| dc.contributor.author | Liu, Yunkai | |
| dc.contributor.author | Alatawi, Abdularaham | |
| dc.contributor.department | School of Criminal Justice | en_US |
| dc.contributor.program | Computer Science | en_US |
| dc.date.accessioned | 2020-05-26T14:47:27Z | |
| dc.date.available | 2020-05-26T14:47:27Z | |
| dc.description.abstract | Unigram is a fundamental element of n-gram in natural language processing. However, unigrams collected from a natural language corpus are unsuitable for solving problems in the domain of computer programming languages. In this paper, we analyze the properties of unigrams collected from an ultra-large source code repository. Specifically, we have collected 1.01 billion unigrams from 0.7 million open source projects hosted at GitHub.com. By analyzing these unigrams, we have discovered statistical patterns regarding (1) how developers name variables, methods, and classes, and (2) how developers choose abbreviations. Our study describes a probabilistic model for solving a well-known problem in source code analysis: how to expand a given abbreviation to its original indented word. It shows that the unigrams collected from source code repositories are essential resources to solving the domain specific problems. | en_US |
| dc.description.uri | https://www.bowiestate.edu/files/resources/abdul-alatawi-research-paper-bigmm2017.pdf | en_US |
| dc.format.extent | pages 8 | en_US |
| dc.genre | conference paper | en_US |
| dc.identifier | doi:10.13016/m2d58q-xtmy | |
| dc.identifier.uri | DOI 10.1109/BigMM.2017.13 | |
| dc.identifier.uri | http://hdl.handle.net/11603/18741 | |
| dc.language.iso | en | en_US |
| dc.relation.isAvailableAt | University of Baltimore | |
| dc.subject | programming language | en_US |
| dc.subject | Source Code | en_US |
| dc.subject | n-gram | en_US |
| dc.subject | unigram | en_US |
| dc.subject | abbreviations | en_US |
| dc.title | Statistical Unigram Analysis for Source Code Repository | en_US |
| dc.title.alternative | 2017 IEEE Third International Conference on Multimedia Big Data | en_US |
| dc.type | Event | en_US |
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