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Journal of Education For Sustainable Innovation
Article . 2024 . Peer-reviewed
License: CC BY SA
Data sources: Crossref
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Large language models in software engineering

A systematic review and vision
Authors: Nguyen Van Viet; Nguyen The Vinh;

Large language models in software engineering

Abstract

Large Language Models (LLMs) are very large deep learning models pre-trained on a vast amount of data. This article aims to provide an overview of the use of major language models in the field of software engineering from January 2021 to February 2024. It surveys the emerging area of Large Language Modeling in Software Engineering but acknowledges that to fully understand the issues, effects, and limitations of LLMs in this field, further research is needed. The article also highlights open research challenges for applying Large Language Models to technical problems faced by software engineers. The exceptional properties of LLMs bring novelty and creativity to applications within Software Engineering activities, including coding, design, requirements, repair, refactoring, performance improvement, documentation, and analytics. Our survey demonstrates the key role of reliable and efficient large language models in the development and deployment of Software Engineering.

  • BIP!
    Impact byBIP!
    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).
    4
    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).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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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!
4
Top 10%
Average
Average
gold