Large language models for code completion: A systematic literature review
<p>Code completion serves as a fundamental aspect of modern software development, improving developers' coding processes. Integrating code completion tools into an Integrated Development Environment (IDE) or code editor enhances the coding process and boosts productivity by reducing error...
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
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| _version_ | 1864513555997392896 |
|---|---|
| author | Rasha Ahmad Husein (19744756) |
| author2 | Hala Aburajouh (19744759) Cagatay Catal (6897842) |
| author2_role | author author |
| author_facet | Rasha Ahmad Husein (19744756) Hala Aburajouh (19744759) Cagatay Catal (6897842) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rasha Ahmad Husein (19744756) Hala Aburajouh (19744759) Cagatay Catal (6897842) |
| dc.date.none.fl_str_mv | 2024-08-26T21:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.csi.2024.103917 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Large_language_models_for_code_completion_A_systematic_literature_review/27109912 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Data management and data science Software engineering Code completion Large language models Deep learning Transformers |
| dc.title.none.fl_str_mv | Large language models for code completion: A systematic literature review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Code completion serves as a fundamental aspect of modern software development, improving developers' coding processes. Integrating code completion tools into an Integrated Development Environment (IDE) or code editor enhances the coding process and boosts productivity by reducing errors and speeding up code writing while reducing cognitive load. This is achieved by predicting subsequent tokens, such as keywords, variable names, types, function names, operators, and more. Different techniques can achieve code completion, and recent research has focused on Deep Learning methods, particularly Large Language Models (LLMs) utilizing Transformer algorithms. While several research papers have focused on the use of LLMs for code completion, these studies are fragmented, and there is no systematic overview of the use of LLMs for code completion. Therefore, we aimed to perform a Systematic Literature Review (SLR) study to investigate how LLMs have been applied for code completion so far. We have formulated several research questions to address how LLMs have been integrated for code completion-related tasks and to assess the efficacy of these LLMs in the context of code completion. To achieve this, we retrieved 244 papers from scientific databases using auto-search and specific keywords, finally selecting 23 primary studies based on an SLR methodology for in-depth analysis. This SLR study categorizes the granularity levels of code completion achieved by utilizing LLMs in IDEs, explores the existing issues in current code completion systems, how LLMs address these challenges, and the pre-training and fine-tuning methods employed. Additionally, this study identifies open research problems and outlines future research directions. Our analysis reveals that LLMs significantly enhance code completion performance across several programming languages and contexts, and their capability to predict relevant code snippets based on context and partial input boosts developer productivity substantially.</p><h2>Other Information</h2> <p> Published in: Computer Standards & Interfaces<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.csi.2024.103917" target="_blank">https://dx.doi.org/10.1016/j.csi.2024.103917</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_ec56df288664d133551140d0bc8b0873 |
| identifier_str_mv | 10.1016/j.csi.2024.103917 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27109912 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Large language models for code completion: A systematic literature reviewRasha Ahmad Husein (19744756)Hala Aburajouh (19744759)Cagatay Catal (6897842)Information and computing sciencesArtificial intelligenceData management and data scienceSoftware engineeringCode completionLarge language modelsDeep learningTransformers<p>Code completion serves as a fundamental aspect of modern software development, improving developers' coding processes. Integrating code completion tools into an Integrated Development Environment (IDE) or code editor enhances the coding process and boosts productivity by reducing errors and speeding up code writing while reducing cognitive load. This is achieved by predicting subsequent tokens, such as keywords, variable names, types, function names, operators, and more. Different techniques can achieve code completion, and recent research has focused on Deep Learning methods, particularly Large Language Models (LLMs) utilizing Transformer algorithms. While several research papers have focused on the use of LLMs for code completion, these studies are fragmented, and there is no systematic overview of the use of LLMs for code completion. Therefore, we aimed to perform a Systematic Literature Review (SLR) study to investigate how LLMs have been applied for code completion so far. We have formulated several research questions to address how LLMs have been integrated for code completion-related tasks and to assess the efficacy of these LLMs in the context of code completion. To achieve this, we retrieved 244 papers from scientific databases using auto-search and specific keywords, finally selecting 23 primary studies based on an SLR methodology for in-depth analysis. This SLR study categorizes the granularity levels of code completion achieved by utilizing LLMs in IDEs, explores the existing issues in current code completion systems, how LLMs address these challenges, and the pre-training and fine-tuning methods employed. Additionally, this study identifies open research problems and outlines future research directions. Our analysis reveals that LLMs significantly enhance code completion performance across several programming languages and contexts, and their capability to predict relevant code snippets based on context and partial input boosts developer productivity substantially.</p><h2>Other Information</h2> <p> Published in: Computer Standards & Interfaces<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.csi.2024.103917" target="_blank">https://dx.doi.org/10.1016/j.csi.2024.103917</a></p>2024-08-26T21:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.csi.2024.103917https://figshare.com/articles/journal_contribution/Large_language_models_for_code_completion_A_systematic_literature_review/27109912CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/271099122024-08-26T21:00:00Z |
| spellingShingle | Large language models for code completion: A systematic literature review Rasha Ahmad Husein (19744756) Information and computing sciences Artificial intelligence Data management and data science Software engineering Code completion Large language models Deep learning Transformers |
| status_str | publishedVersion |
| title | Large language models for code completion: A systematic literature review |
| title_full | Large language models for code completion: A systematic literature review |
| title_fullStr | Large language models for code completion: A systematic literature review |
| title_full_unstemmed | Large language models for code completion: A systematic literature review |
| title_short | Large language models for code completion: A systematic literature review |
| title_sort | Large language models for code completion: A systematic literature review |
| topic | Information and computing sciences Artificial intelligence Data management and data science Software engineering Code completion Large language models Deep learning Transformers |