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|Title=NL+SE 2016 : Natural language processing and software engineering | |Title=NL+SE 2016 : Natural language processing and software engineering | ||
|Type=Conference | |Type=Conference | ||
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|Homepage=nlse-fse.github.io/ | |Homepage=nlse-fse.github.io/ | ||
|City=Seattle | |City=Seattle | ||
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|Start Date=2016/11/13 | |Start Date=2016/11/13 | ||
|End Date=2016/11/13 | |End Date=2016/11/13 | ||
| + | |Academic Field=NLP;Software Engineering | ||
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Natural Language Processing (NLP) techniques and tools have become very powerful and are applicable in many domains. In the context of Software Engineering (SE), there are many promising opportunities for the application of NLP to be used to improve SE theory and practice. Recently, investigations have begun to unravel the extent to which large code corpora that can be retrieved from GitHub, StackOverflow, etc., are amenable to analysis using statistical NLP models and algorithms, so that the revolutionary advances in speech recognitions, translation, comprehension, etc. can be applied in SE. | Natural Language Processing (NLP) techniques and tools have become very powerful and are applicable in many domains. In the context of Software Engineering (SE), there are many promising opportunities for the application of NLP to be used to improve SE theory and practice. Recently, investigations have begun to unravel the extent to which large code corpora that can be retrieved from GitHub, StackOverflow, etc., are amenable to analysis using statistical NLP models and algorithms, so that the revolutionary advances in speech recognitions, translation, comprehension, etc. can be applied in SE. | ||
Revision as of 13:49, 24 August 2022
Natural Language Processing (NLP) techniques and tools have become very powerful and are applicable in many domains. In the context of Software Engineering (SE), there are many promising opportunities for the application of NLP to be used to improve SE theory and practice. Recently, investigations have begun to unravel the extent to which large code corpora that can be retrieved from GitHub, StackOverflow, etc., are amenable to analysis using statistical NLP models and algorithms, so that the revolutionary advances in speech recognitions, translation, comprehension, etc. can be applied in SE.
This workshop will bring together an international group of researchers in Statistical NLP, Programming Languages, Software Engineering and related fields for an intensive period of discussion and presentation of results in the area. We invite a range of researchers with both NLP and SE backgrounds to come together, discuss their research, establish datasets, tasks, and baselines, and generally help the field build momentum.
We invite short position papers, of at most 4 pages in length. Submissions will be reviewed primarily for relevance, will not appear in ACM Digital Library, and may be published subsequently elsewhere. A few of the submissions will be invited for presentation.
