Taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software

The constant growth of the software industry has driven companies to explore new ways to improve their processes, generating novel techniques to optimize the tasks involved in software development, in order to increase the efficiency of these processes. At the same time, the terms “Artificial Intell...

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Autores principales: Enciso Rolon, Alex Paul, González Prieto, Osvaldo, Barán, Benjamín
Formato: Objeto de conferencia
Lenguaje:Español
Publicado: 2024
Materias:
Ciencias Informáticas
Machine Learning
Software Engineering
Taxonomy
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/177035
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SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-177035
record_format dspace
spelling I19-R120-10915-1770352025-02-28T20:06:36Z http://sedici.unlp.edu.ar/handle/10915/177035 Taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software Enciso Rolon, Alex Paul González Prieto, Osvaldo Barán, Benjamín 2024-08 2024 2025-02-28T12:21:14Z es Ciencias Informáticas Machine Learning Software Engineering Taxonomy The constant growth of the software industry has driven companies to explore new ways to improve their processes, generating novel techniques to optimize the tasks involved in software development, in order to increase the efficiency of these processes. At the same time, the terms “Artificial Intelligence” and “Machine Learning” (ML), are being increasingly used, but there still is a certain lack of knowledge about these concepts. Given this context, our main objective is to establish a connection between these disciplines, in order to better understand the benefit of using ML in Software Engineer. In this work, a systematic analysis of the scientific literature published between 2018 and 2023 has been carried out in order to create a taxonomy of Machine Learning algorithms applied to the stages required for software development. The most prominent results indicate that the testing phase in the software development cycle is one of the most researched areas in relation to the aforementioned challenges. Furthermore, it has been observed that some ML algorithms such as Random Forest demonstrate acceptable performance in optimizing one or more tasks simultaneously in the software development process. Sociedad Argentina de Informática e Investigación Operativa Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 86-99
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Español
topic Ciencias Informáticas
Machine Learning
Software Engineering
Taxonomy
spellingShingle Ciencias Informáticas
Machine Learning
Software Engineering
Taxonomy
Enciso Rolon, Alex Paul
González Prieto, Osvaldo
Barán, Benjamín
Taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software
topic_facet Ciencias Informáticas
Machine Learning
Software Engineering
Taxonomy
description The constant growth of the software industry has driven companies to explore new ways to improve their processes, generating novel techniques to optimize the tasks involved in software development, in order to increase the efficiency of these processes. At the same time, the terms “Artificial Intelligence” and “Machine Learning” (ML), are being increasingly used, but there still is a certain lack of knowledge about these concepts. Given this context, our main objective is to establish a connection between these disciplines, in order to better understand the benefit of using ML in Software Engineer. In this work, a systematic analysis of the scientific literature published between 2018 and 2023 has been carried out in order to create a taxonomy of Machine Learning algorithms applied to the stages required for software development. The most prominent results indicate that the testing phase in the software development cycle is one of the most researched areas in relation to the aforementioned challenges. Furthermore, it has been observed that some ML algorithms such as Random Forest demonstrate acceptable performance in optimizing one or more tasks simultaneously in the software development process.
format Objeto de conferencia
Objeto de conferencia
author Enciso Rolon, Alex Paul
González Prieto, Osvaldo
Barán, Benjamín
author_facet Enciso Rolon, Alex Paul
González Prieto, Osvaldo
Barán, Benjamín
author_sort Enciso Rolon, Alex Paul
title Taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software
title_short Taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software
title_full Taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software
title_fullStr Taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software
title_full_unstemmed Taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software
title_sort taxonomía de algoritmos basados en machine learning aplicados en la ingeniería de software
publishDate 2024
url http://sedici.unlp.edu.ar/handle/10915/177035
work_keys_str_mv AT encisorolonalexpaul taxonomiadealgoritmosbasadosenmachinelearningaplicadosenlaingenieriadesoftware
AT gonzalezprietoosvaldo taxonomiadealgoritmosbasadosenmachinelearningaplicadosenlaingenieriadesoftware
AT baranbenjamin taxonomiadealgoritmosbasadosenmachinelearningaplicadosenlaingenieriadesoftware
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