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Integrating Natural Language Processing and Software Engineering

Authors: Nakul Sharma; Prasanth Yalla;

Integrating Natural Language Processing and Software Engineering

Abstract

This paper tries to put various ways in which Natural Language Processing (NLP) and Software Engineering (SE) can be seen as inter-disciplinary research areas. We survey the current literature, with the aim of assessing use of Software Engineering and Natural Language Processing tools in the researches undertaken. An assessment of how various phases of SDLC can employ NLP techniques is presented. The paper also provides the justification of the use of text for automating or combining both these areas. A short research direction while undertaking multidisciplinary research is also provided.

Keywords

Software/trends, Software Validation, software code, Software development, software testing, integration, Software Design, Software/standards, Software/history, Software, Software/classification, software maintainence

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Average
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