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żCómo influye el uso de la inteligencia artificial en el desarrollo de software?=

How = does the use of artificial intelligence influence software development?

Diego Omar Guerra Poalasin

https://= orcid.org/0000-0003-0408-8655

Universidad Técnica de Ambato=

dguerra6729@uta.edu.ec

Francisco Israel Ruales Flore= s https://orcid.org/0009-0= 001-9231-7229

Universidad Técnica de Ambato=

fruales3566@uta.edu.ec

	Artículo de Investigación Científica y Tecnológica Enviado: 13/07/2024 Revisado: 21/08/2024 Aceptado: 10/09/2024 Publicado:05/10/2024 DOI: https://doi.org/10.33262/ct.v3i4.53

Cítese:<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",= serif; mso-fareast-font-family:"Times New Roman";mso-font-kerning:0pt;mso-ligatu= res: none;mso-ansi-language:ES-EC;mso-fareast-language:ES'>		Guerra Poalasin, D. O., & Ruales Flores, F. I. (2024). żCómo infl= uye el uso de la inteligencia artificial en el desarrollo de software?. Cienc= ia & Turismo, 3(4), 56-69. https://doi.org/10.33262/ct.v3i4.53
		*CIENCIA & TURISMO,* es una revista multidisciplinaria, trimestral, que se publicará en soporte electrónico tiene como misión contribuir a la formació= n de profesionales competentes con visión humanística y crítica que sean capac= es de exponer sus resultados investigativos y científicos en la misma medida= que se promueva mediante su intervención cambios positivos en la sociedad.&nb= sp; La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registrada en la Cámara Ecuatoriana de Li= bro con No de Afiliación 663)
		Esta revista está protegida bajo una licencia Creative Commons AttributionNonCommercialNoDerivatives 4.0 International. Copia de la licencia: http://creativecommons.org/licenses/by-nc-nd/4.0/<= /i>
Palabras claves: Inteligencia artificial, software, tecnología, desarrolladores, implementación		Resum= en Introducción: Se presenta un panorama general sobre la integración de la IA en el desarrol= lo de software, destacando su creciente relevancia en tareas como la generac= ión de código, pruebas automatizadas y mantenimiento. Asimismo, se examina su capacidad para transformar la industria tecnológica y fomentar la innovac= ión. Objetivos: El estudio tiene = como propósito identificar los principales aportes de la IA al desarrollo de software, evaluar su impacto en la productividad y en las competencias de= los desarrolladores, y explorar las implicaciones económicas y sociales deriv= adas de su implementación. Metodologí= a: Se utilizó un enfoque cualitativo y cuantitativo, analizando estudios previos, encuestas a desarrolladores y datos sobre herramientas basadas en IA. Además, se investigaron casos de uso en empresas tecnológicas para comprender cómo se aplican estas herramientas en contextos reales. Resultados: Los hallazgos revel= an que la IA mejora significativamente la eficiencia del desarrollo de software = al automatizar tareas repetitivas, reducir errores y acelerar la entrega de productos. También fomenta el aprendizaje continuo y la colaboración humano-máquina, permitiendo a los desarrolladores enfocarse en tareas más complejas y creativas. Para las empresas, la IA representa una ventaja competitiva al reducir costos y democratizar el acceso al desarrollo tecnológico. Conclusiones: L= a IA está redefiniendo el desarrollo de software, generando beneficios tangibl= es en términos de productividad, accesibilidad y crecimiento económico. Sin embargo, su implementación plantea desafíos éticos y técnicos que requier= en una gestión adecuada. Promover un uso equilibrado y responsable de estas herramientas será clave para maximizar su impacto positivo en el futuro d= e la industria tecnológica. Área de estudio general: Tecnología. Área de estudio específica: Desarrollo de softw= are. Tipo de estudio: Artículo revisión bibliográfica
Keywords: Artificial intelligence, software, technolo= gy, developers, implementation		Abstract In= troduction: An overview of the integration of AI in software development is presented, highlighting its growing relevance in tasks such as code generation, automated testing, and maintenance. It also examines its ability to trans= form the technology industry and foster innovation. Objectives: The study aims to identify the main contributions= of AI to software development, assess its impact on developer productivity a= nd skills, and explore the economic and social implications of its implementation. Methodology:= A qualitative and quantitative approach was used, analyzing previous studie= s, developer surveys, and data on AI-based tools. In addition, use cases in technology companies were investigated to understand how these tools are applied in real-world contexts. = Results: The findings reveal that AI significantly improves the efficiency of soft= ware development by automating repetitive tasks, reducing errors, and accelera= ting product delivery. It also fosters continuous learning and human-machine collaboration, allowing developers to focus on more complex and creative tasks. For companies, AI represents a competitive advantage by reducing c= osts and democratising access to technological development. Conclusions: AI is redefining software development, generating tangible benefits in terms of productivity, accessibility and economic growth. However, its implementation poses ethical and technical challenges that require proper management. Promoting a balanced and responsible use = of these tools will be key to maximising their positive impact on the future= of the technology industry.

<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES-EC'>1.&n= bsp; Introducc= ión

La inteligencia artifici= al (IA) ha emergido como una de las tecnologías más transformadoras de las últ= imas décadas, revolucionando múltiples sectores, incluido el desarrollo de softw= are. Su implementación en este campo ha permitido optimizar procesos, mejorar la calidad de los productos y reducir significativamente los tiempos de desarrollo. Herramientas basadas en IA, como los asistentes de codificación, los sistemas de prueba automatizados y los modelos predictivos, están redefiniendo la manera en que los desarrolladores diseńan, prueban y mantie= nen aplicaciones.

Esta integración ha dado paso a una nueva era donde las tareas de programación, análisis de sistemas= , y gestión de proyectos se optimizan mediante algoritmos inteligentes, capaces= de aprender, adaptarse y ejecutar tareas con una eficiencia sin precedentes. L= a IA no solo ha revolucionado la forma en que se diseńan y desarrollan las aplicaciones, sino que también ha mejorado significativamente la experienci= a de los desarrolladores y la eficiencia de los procesos de desarrollo. <= !--[if supportFields]>ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemDa= ta":{"author":[{"dropping-particle":"",&= quot;family":"Huet","given":"Pablo",&quo= t;non-dropping-particle":"","parse-names":false,&q= uot;suffix":""}],"container-title":"OpenWebin= ars.net","id":"ITEM-1","issued":{"d= ate-parts":[["2024"]]},"note":"Accessed: 2024-12-11","title":"Inteligencia artificial en desarro= llo de software: Tendencias emergentes y futuro","type":"ar= ticle"},"uris":["http://www.mendeley.com/documents/?uui= d=3D46a1e696-89d0-4c1f-9d86-1e0d86a33880"]}],"mendeley":{&qu= ot;formattedCitation":"(Huet, 2024)","plainTextFormattedCitation":"(Huet, 2024)","previouslyFormattedCitation":"(Huet, 2024)"},"properties":{"noteIndex":0},"schema&= quot;:"https://github.com/citation-style-language/schema/raw/master/cs= l-citation.json"}(Huet, 2024)

El nuevo acercamiento es construir sistemas que aprenden de sí mismos, convirtiéndose en expertos que modelan y abstraen reglas a partir de los datos que les son alimentados. Di= chos sistemas van mejorando en su precisión, se adaptan a lo desconocido y expan= den sus capacidades más allá de la programación original. Las técnicas tradicionales de procesamiento de lenguaje natural (NLP), razonamiento basa= do en reglas y representación de conocimiento están siendo aumentadas con el machine learning —especialmente deep learning— para mejorar la IA (ver figu= ra 1). Los resultados preliminares son prometedores: estamos viendo surgir nue= vas apps con cierta “inteligencia” en una gran variedad de dominios.=

Esta ola de inteligencia artificial impactará el trabajo de los desarrolladores de software, así que= es importante estar preparados. Los desarrolladores deben entender en qué consisten dichas tecnologías y cómo pueden aplicarlas, tanto en el ciclo de vida de desarrollo de software como en las aplicaciones mismas. (Galván, 2023)

El impacto laborar de la= IA en la industria del desarrollo de software es un tema de gran relevancia y debate. Mientras la IA puede ofrecer herramientas poderosas que facilitan la entrada de programadores principiantes al campo, también plantea preguntas sobre la seguridad laboral a largo plazo para estos mismos profesionales.

Los profesionales se sue= len adentrar en este mundo realizando tareas repetitivas y con bajo valor productivo por lo que puede llevar a una disminución de la demanda de profesionales con este tipo de rol.

Es importante hacer ver a las empresas que dichos profesionales se convertirán en un gran valor ańadi= do pasado un tiempo ya que habrán adquirido los conocimientos necesarios tanto= a nivel técnico como a nivel de producto siendo muy difícil de reemplazar por= una inteligencia artificial. (Rodríguez, 2024)

(Mármol, 2023) dice lo siguiente: “No = cabe duda de que, en la actualidad, tener acceso a herramientas de inteligencia artificial es como tener un asistente que le pasas el trabajo y te lo gener= a en cuestión de segundos. Esto resulta especialmente útil para los desarrollado= res que necesitan diseńar algoritmos que sean capaces de analizar grandes cantidades de información compleja y construir modelos que sean capaces de reaccionar a ella con tareas específicas”.

Para (García, 2023), la IA es una herramienta que está favoreciendo otras tecnologías inteligentes, demostrando que en la actualidad la tendencia es buscar la interacción entre todos los campos de la tecnología para desarrollar en conjunto una tecnología cada vez más sofisticada, inteligente e inclusiva. =

No obstante, el impacto = de la inteligencia artificial en el desarrollo de software no está exento de desafíos. Aspectos como la dependencia tecnológica, la ética en la toma de decisiones automatizadas y la necesidad de capacitar a los desarrolladores = para interactuar con estas herramientas plantean nuevas interrogantes. Este artí= culo tiene como objetivo analizar cómo la inteligencia artificial influye en cada etapa del ciclo de vida del software, evaluando tanto sus beneficios como l= as barreras que enfrenta su adopción. A través de un enfoque integral, se busca explorar las implicaciones técnicas, económicas y sociales de esta tecnolog= ía en el futuro del desarrollo de software.

<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES-EC'>2.&n= bsp; Metodolog= ía

Esta investigación tiene= un enfoque mixto cualitativo y cuantitativo. Es cualitativo puesto se basó en fundamentación teórica y revisión bibliográfica. Lo cuantitativo se realizó= a través de datos obtenidos en páginas especializadas en estadísticas de uso = de aplicaciones y redes sociales que han aportado al desarrollo del tema. Este trabajo cuenta con un nivel descriptivo, puesto se ha realizado en virtud d= e la doctrina y análisis emitido por autores que han tratado al respecto. Los métodos aplicados han sido el inductivo – deductivo, partiendo de ideas particulares hasta llegar a generales.

<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; mso-fareast-font-family:"Times New Roman";mso-ansi-language:ES-EC'>3.&n= bsp; Resultado= s

La inteligencia artifici= al permite automatizar tareas repetitivas como las pruebas y la generación de código, optimizando el flujo de trabajo en el desarrollo de software. A tra= vés de algoritmos de aprendizaje automático, es posible analizar grandes cantid= ades de datos históricos para detectar patrones, errores comunes y tendencias, facilitando que los desarrolladores se enfoquen en actividades más compleja= s y creativas. Por ejemplo, las herramientas de prueba automatizadas impulsadas= por IA pueden crear casos de prueba de forma automática, ejecutarlos eficientem= ente y proporcionar resultados precisos, lo que acelera el ciclo de desarrollo y mejora la calidad del producto final. Algunas de las herramientas que permi= ten lograr estos avances son: (Solvex Dominicana, 2024)

Tabla 1<= /b>

Herramientas con inteligencia artificial

Herramienta

Descripción

Selenium

Es una herramienta de código abierto que automatiza los navegadores web para pruebas de softwar= e.

Tabla 1<= /b>

Herramientas con inteligencia artificial (= continuación)

Herramienta

Descripción

Testim

Ofrece pruebas de inteligencia artificial que pueden escribir, mantener y ejecutar automáticamente pruebas de UI para aplicaciones web.

Mabl

Proporciona pruebas de extremo a extremo que se adaptan automáticamente a los cambios en la inte= rfaz de usuario utilizando aprendizaje automático.

Herramientas para la productividad

Visual Studio IntelliCode<= /p>

Una extensión de Visual Studio que utiliza modelos de aprendizaje automático para ofrecer sugeren= cias de código inteligentes mientras escribes.

TabNine

Un plugin para varios editores de código que utiliza inteligencia artificial para autocompletar código de manera eficiente y precisa.

DeepCode

Utiliza IA para analiz= ar el código en busca de errores y ofrecer recomendaciones para mejorar la calidad y seguridad del código.

Herramientas= de optimización

Jira Software

Incorpora característi= cas de inteligencia artificial para optimizar la gestión de proyectos, asignar tareas automáticamente y predecir plazos de entrega.

GitPrime (ahora parte de Pluralsight Flow)<= /o:p>

Utiliza IA para analiz= ar datos de repositorios de código y ofrecer insights sobre la productividad= del equipo, la calidad del código y los cuellos de botella en el flujo de trabajo.

GitLab Auto DevOps

Automatiza el proceso = de CI/CD (Integración Continua / Entrega Continua) utilizando IA para identificar y corregir problemas en el pipeline de desarrollo.=

Tabla 1<= /b>

Herramientas con inteligencia artificial (= continuación)

Herramientas= de detección y corrección de errores

AI Code Solver

Es una herramienta sofisticada que aprovecha la inteligencia artificial para mejorar el proc= eso de codificación. Analiza código utilizando una variedad de técnicas, incl= uido el análisis estático y dinámico, para detectar errores, sugerir mejoras e incluso automatizar algunas tareas de codificación.

Code AI

Está diseńado para mej= orar el proceso de codificación al proporcionar fragmentos de código generados= por IA y funciones inteligentes de finalización de código, con el objetivo de acelerar el proceso de desarrollo y reducir los errores.

Fuente: (Solvex Dominicana, 2024)

Tipos de Inteligencia Artificial en el desarrollo = de software

La IA en el desarrollo se integra en varias etapas del ciclo de desarrollo de software, permitiendo a= los desarrolladores construir aplicaciones más inteligentes y eficientes.<= /o:p>

Puedes encontrar oportunidades para utilizar estas tecnologías en tus proyectos de desarroll= o de software. (Heinsohn, 2023)

Aquí tienes algunos de l= os más comunes que puedes utilizar:

·         Aprendiza= je Automático (Machine Learning, ML): Los algoritmos de ML permiten que el software aprenda a partir de los datos y mejore su rendimiento con el tiempo.

·         Procesami= ento de Lenguaje Natural (Natural Language Processing, NLP): El NLP permite que el software comprenda y procese el lenguaje humano.

·         Visión por Computadora (Computer Vision): La visión por computadora se centra en permitir = que las máquinas vean e interpreten imágenes o videos.

·         Sistemas Expertos (Expert Systems): Los sistemas expertos utilizan técnicas basadas en conocimiento para imitar la habilidad humana en un dominio específico.

·         Inteligen= cia de Negocios (Business Intelligence, BI): La inteligencia de negocios implica el uso= de técnicas de IA para analizar e interpretar grandes volúmenes de datos y tom= ar decisiones empresariales informadas.

·         Análisis Predictivo (Predictive Analytics): Utilizando algoritmos de IA y técnicas estadísti= cas, el análisis predictivo examina datos pasados y pronostica resultados futuro= s.

Beneficios de la IA para programar: programadores y empresas

El uso de la inteligencia artificial ha transformado significativamente el desarrollo de software, brindando ventajas tanto a programadores individuales como a empresas. Esta tecnología ha optimizado procesos, mejorado la eficiencia y facilitado la creación de soluciones más robustas y personalizadas. (Escoba= r, 2024)

Figura 1=

Beneficios de la IA para programar

Fuente: (Escoba= r, 2024)

Según una investigación realizada por GitHub y Keystone.AI, se determinó que los desarrolladores aceptaron el 30% de las sugerencias de código, lo que les permitió completar tareas un 55% más rápido. (Bejarano, 2024)

= Más Productividad =3D Más impulso económico

El incremento en la productividad impulsado por el desarrollo de la inteligencia artificial pod= ría ańadir más de $1.5 billones al PIB global. Con un aumento proyectado del 30= % en la productividad y un estimado de 45 millones de desarrolladores profesiona= les para 2030, las herramientas de desarrollo generativo basadas en IA podrían equivaler a sumar 15 millones de "desarrolladores efectivos" adicionales a la capacidad mundial. Este aumento representaría una contribu= ción sustancial a la actividad económica generada por este sector de trabajadore= s, destacando el impacto transformador de la IA en la economía global.

Figura 2=

Ganancias de la IA en el desarrollo de software

=

Fuente: (Griffi= ths, 2023)

A medida que los desarrolladores adopten herramientas para potenciar sus habilidades, adquirirán mayor destreza en el uso e interacción con la IA, lo que optimizará el ciclo de vida del desarro= llo. Esto contribuirá a democratizar el acceso al desarrollo de software, reduci= r la brecha laboral en el sector y convertir las herramientas de programación asistidas por IA en un componente esencial de la formación educativa estánd= ar para los desarrolladores.

Figura 3=

Porcentaje de aceptación de herramientas c= on IA

=

Fuente: (Griffi= ths, 2023)

Los desarrolladores seńalaron que el principal beneficio de las herramientas de codificación basadas en IA era el fortalecimiento de sus habilidades en lenguajes de programación, lo que, según ellos, contribuye a tener una jornada laboral m= ás satisfactoria. Un objetivo del uso de la inteligencia artificial se centra precisamente en promover desarrolladores más productivos, competentes y contentos con su trabajo.

Figura 4=

Productividad de los desarrolladores =

=

Ejemplos de aplicación de la IA al desarrollo de s= oftware

Un ejemplo de la aplicac= ión de AI al trabajo de los desarrolladores es directamente la de generar códig= o de aplicación.

En este caso, se necesita escribir código Powershell para el plugin SnipDo de Windows. Se solicitó Ch= atGPT4 que escribiera este código.

Este es el prompt donde = se pide que escriba el código:

“Escribe el código PowerShell que transforme una cadena de texto de entrada en el equivalente unicode con aspecto de negrita= . “

Y este es el resultado d= el codigo:

Figura 5=

Repuesta de código solicitado a CHATGPT

=

= Fuente: (Ballarin, 2024)

=

=

4.&n= bsp;     Conclusión

·         La implementación de inteligencia artificial ha transformado las eta= pas del ciclo de desarrollo, desde la codificación hasta las pruebas y el mantenimiento. Herramientas basadas en IA permiten automatizar tareas repetitivas, detectar errores con mayor precisión y acelerar la entrega de productos, lo que resulta en una mayor eficiencia y calidad en el desarroll= o de software.

·         El uso de la IA ha cambiado las competencias necesarias en los desar= rolladores, orientándolos hacia tareas más creativas y estratégicas. A medida que las herramientas de IA se integran en la práctica diaria, se fomenta el aprendi= zaje continuo y la capacidad de trabajar en colaboración con sistemas inteligent= es, potenciando la productividad y la satisfacción laboral.
5.      La inteligencia artificial= no solo mejora la productividad de los equipos, sino que también democratiza el acceso al desarrollo de software, permitiendo que personas con conocimientos limitados en programación puedan participar en la creación de soluciones tecnológicas. Además, este avance tiene el potencial de generar un impacto económico significativo, impulsando la innovación y contribuyendo al crecimiento global del sector tecnológico.

6.&n= bsp;     Conflicto de i= ntereses

Los autores declaran que no existe conflicto de intereses.

7.&n= bsp;     Decl= aración de contribución de los autores

Todos autores contribuyeron significativamente en la elaboración del artículo.

8.      Costos de financiamiento

La presente investigación fue financiada en su totalidad con fondos propios de los autores.

9.&n= bsp;     Referencias bibliograficas
Ballarin, A. (2024). Cómo la {IA} potencia a los desarrolladores de software? In Linkedin.com. https://es.linkedin.com/pulse/cóm= o-la-ia-potencia-los-desarrolladores-de-software-ballarin--y0alf=

Bejarano, J. G. (2024). Cómo será tu desarrollo de softwa= re si no incorporas inteligencia artificial? In Computing. https://es.linkedin.com/pulse/cómo-la-ia-potencia-los-desarrolladores-de-so= ftware-ballarin--y0alf

Escobar, C. H. (2024). Futuro y beneficios de la {IA} para programar. In OpenSistemas. https://opensistemas.com/futuro-y-beneficios-de-la-ia-para-programar/<= /o:p>

Galván, P. (2023). Impacto de la Inteligencia Artificial = en el Desarrollo de Software. In <= i>{SG} Buzz. https://sg.com.mx/revista/56/inteligencia-artificial-desarrollo-software

García, J. (2023). Impacto de {IA} en el desarrollo= de software. In Telcel.com.= https://www.telcel.com/empresas/tendencias/notas/desarrollo-software-con-i= a

Griffiths, A. L. (2023). {GitHub} Copilot: Un Ańo revolucionando la {IA}. In <= i>{DEV} Community. https://dev.to/github/github-copilot-un-ano-revolucionando-la-ia-466b<= /o:p>

Heinsohn. (2023). Desarrollo De Software Con Inteligencia Artificial. In Heinsohn. Heinsohn Business Technology. https://www.heinsohn.co/blog/desarrollo-de-software-con-inteligencia-artifi= cial/

Huet, P. (2024). Inteligencia artificial en desarrollo de software: Tendencias emergentes y futuro. In OpenWebinars.net. https://openwebinars.net/blog/inteligencia-artificial-en-desarrollo-de-soft= ware/

Mármol, J. (2023). Cómo la {IA} impulsa la nueva era del desarrollo de software. In <= i>Microsoft.com. https://news.microsoft.com/es-xl/como-la-ia-impulsa-la-nueva-era-del-desarr= ollo-de-software/

Rodríguez, E. (2024). Cómo la inteligencia artificial está transformando el desarrollo de software. In Blog de hiberus. Hiberus. https://www.hiberus.com/crecemos-contigo/inteligencia-artificial-desarrollo= -software/

Solvex Dominicana, S. R. L. (2024). El Impacto de la Inteligencia Artificial en el Proceso de Desarrollo de Software. In Linkedin.com. https://es.linkedin.com/pulse/el-impacto-de-la-inteligencia-artificial-en-p= roceso-desarrollo-75ste

El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento = de la Revista Ciencia & Turismo.<= o:p>

El artículo queda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autoriz= ado por el director de la Revista Cien= cia & Turismo.

=

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Herramienta	Descripción
Selenium	Es una herramienta de código abierto que automatiza los navegadores web para pruebas de softwar= e.

Herramienta	Descripción
Testim	Ofrece pruebas de inteligencia artificial que pueden escribir, mantener y ejecutar automáticamente pruebas de UI para aplicaciones web.
Mabl	Proporciona pruebas de extremo a extremo que se adaptan automáticamente a los cambios en la inte= rfaz de usuario utilizando aprendizaje automático.
Herramientas para la productividad
Visual Studio IntelliCode<= /p>	Una extensión de Visual Studio que utiliza modelos de aprendizaje automático para ofrecer sugeren= cias de código inteligentes mientras escribes.
TabNine	Un plugin para varios editores de código que utiliza inteligencia artificial para autocompletar código de manera eficiente y precisa.
DeepCode	Utiliza IA para analiz= ar el código en busca de errores y ofrecer recomendaciones para mejorar la calidad y seguridad del código.
Herramientas= de optimización
Jira Software	Incorpora característi= cas de inteligencia artificial para optimizar la gestión de proyectos, asignar tareas automáticamente y predecir plazos de entrega.
GitPrime (ahora parte de Pluralsight Flow)<= /o:p>	Utiliza IA para analiz= ar datos de repositorios de código y ofrecer insights sobre la productividad= del equipo, la calidad del código y los cuellos de botella en el flujo de trabajo.
GitLab Auto DevOps	Automatiza el proceso = de CI/CD (Integración Continua / Entrega Continua) utilizando IA para identificar y corregir problemas en el pipeline de desarrollo.=

Herramientas= de detección y corrección de errores
AI Code Solver	Es una herramienta sofisticada que aprovecha la inteligencia artificial para mejorar el proc= eso de codificación. Analiza código utilizando una variedad de técnicas, incl= uido el análisis estático y dinámico, para detectar errores, sugerir mejoras e incluso automatizar algunas tareas de codificación.
Code AI	Está diseńado para mej= orar el proceso de codificación al proporcionar fragmentos de código generados= por IA y funciones inteligentes de finalización de código, con el objetivo de acelerar el proceso de desarrollo y reducir los errores.