PERSONA: A personalized model for code recommendation

Code recommendation is an important feature of modern software development tools to improve the productivity of programmers. The current advanced techniques in code recommendation mostly focus on the crowd-based approach. The basic idea is to collect a large pool of available source code, extract th...

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Detalles Bibliográficos
Autores principales:	Tam The Nguyen, Tung Thanh Nguyen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/78dfb556e27240ad93da9e76c9501d60
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id	oai:doaj.org-article:78dfb556e27240ad93da9e76c9501d60
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spelling	oai:doaj.org-article:78dfb556e27240ad93da9e76c9501d602021-11-25T06:13:55ZPERSONA: A personalized model for code recommendation1932-6203https://doaj.org/article/78dfb556e27240ad93da9e76c9501d602021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594850/?tool=EBIhttps://doaj.org/toc/1932-6203Code recommendation is an important feature of modern software development tools to improve the productivity of programmers. The current advanced techniques in code recommendation mostly focus on the crowd-based approach. The basic idea is to collect a large pool of available source code, extract the common code patterns, and utilize the patterns for recommendations. However, programmers are different in multiple aspects including coding preferences, styles, levels of experience, and knowledge about libraries and frameworks. These differences lead to various usages of code elements. When the code of multiple programmers is combined and mined, such differences are disappeared, which could limit the accuracy of the code recommendation tool for a specific programmer. In the paper, we develop a code recommendation technique that focuses on the personal coding patterns of programmers. We propose Persona, a personalized code recommendation model. It learns personalized code patterns for each programmer based on their coding history, while also combines with project-specific and common code patterns. Persona supports recommending code elements including variable names, class names, methods, and parameters. The empirical evaluation suggests that our recommendation tool based on Persona is highly effective. It recommends the next identifier with top-1 accuracy of 60-65% and outperforms the baseline approaches.Tam The NguyenTung Thanh NguyenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tam The Nguyen Tung Thanh Nguyen PERSONA: A personalized model for code recommendation
description	Code recommendation is an important feature of modern software development tools to improve the productivity of programmers. The current advanced techniques in code recommendation mostly focus on the crowd-based approach. The basic idea is to collect a large pool of available source code, extract the common code patterns, and utilize the patterns for recommendations. However, programmers are different in multiple aspects including coding preferences, styles, levels of experience, and knowledge about libraries and frameworks. These differences lead to various usages of code elements. When the code of multiple programmers is combined and mined, such differences are disappeared, which could limit the accuracy of the code recommendation tool for a specific programmer. In the paper, we develop a code recommendation technique that focuses on the personal coding patterns of programmers. We propose Persona, a personalized code recommendation model. It learns personalized code patterns for each programmer based on their coding history, while also combines with project-specific and common code patterns. Persona supports recommending code elements including variable names, class names, methods, and parameters. The empirical evaluation suggests that our recommendation tool based on Persona is highly effective. It recommends the next identifier with top-1 accuracy of 60-65% and outperforms the baseline approaches.
format	article
author	Tam The Nguyen Tung Thanh Nguyen
author_facet	Tam The Nguyen Tung Thanh Nguyen
author_sort	Tam The Nguyen
title	PERSONA: A personalized model for code recommendation
title_short	PERSONA: A personalized model for code recommendation
title_full	PERSONA: A personalized model for code recommendation
title_fullStr	PERSONA: A personalized model for code recommendation
title_full_unstemmed	PERSONA: A personalized model for code recommendation
title_sort	persona: a personalized model for code recommendation
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/78dfb556e27240ad93da9e76c9501d60
work_keys_str_mv	AT tamthenguyen personaapersonalizedmodelforcoderecommendation AT tungthanhnguyen personaapersonalizedmodelforcoderecommendation
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PERSONA: A personalized model for code recommendation

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