[{"data":1,"prerenderedAt":1028},["ShallowReactive",2],{"/es/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity":3,"footer-es":35,"the-source-banner-es":342,"the-source-navigation-es":354,"the-source-newsletter-es":381,"footer-source-/es/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity/":392,"article-site-categories-es":398,"how-ai-helps-devsecops-teams-improve-productivity-article-hero-category-es":400,"how-ai-helps-devsecops-teams-improve-productivity-the-source-source-cta-es":424,"how-ai-helps-devsecops-teams-improve-productivity-category-es":440,"how-ai-helps-devsecops-teams-improve-productivity-the-source-resources-es":453,"how-ai-helps-devsecops-teams-improve-productivity-article-hero-author-es":479},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"config":8,"seo":12,"content":18,"type":26,"category":5,"slug":27,"_id":28,"_type":29,"title":30,"_source":31,"_file":32,"_stem":33,"_extension":34},"/es/the-source/ai/how-ai-helps-devsecops-teams-improve-productivity","ai",false,"",{"layout":9,"template":10,"author":11,"featured":6},"the-source","TheSourceArticle","kristina-weis",{"title":13,"description":14,"ogImage":15,"config":16},"Cómo la IA ayuda a los equipos de DevSecOps a mejorar la productividad","Descubre cómo los equipos de DevOps utilizan la IA para ahorrar tiempo y mejorar la eficiencia.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1751464559/fbvzbz6vxppsblv8sngf.png",{"ignoreTitleCharLimit":17},true,{"title":13,"date":19,"description":14,"timeToRead":20,"heroImage":15,"keyTakeaways":21,"articleBody":25},"2024-01-02","Lectura de 6 min",[22,23,24],"El uso de la IA en los flujos de trabajo de DevSecOps aumenta significativamente la eficiencia y la productividad al automatizar tareas repetitivas, reducir el cambio de contexto y proporcionar asistencia inteligente.","Las herramientas de IA pueden mejorar la calidad y seguridad del código al ofrecer sugerencias en tiempo real, resumir las amenazas potenciales y brindar estrategias de corrección.","Las organizaciones deben desarrollar estrategias que aborden las preocupaciones de privacidad, propiedad intelectual y seguridad de los datos, a fin de garantizar que las implementaciones de IA cumplan con los estándares legales y éticos.","La inteligencia artificial (IA) y el aprendizaje automático (ML) en el desarrollo de software llegaron para quedarse, y los equipos de DevSecOps los utilizan de muchas maneras diferentes para ahorrar tiempo y mejorar la productividad y la eficiencia. \n\nLas siguientes son algunas formas en que los equipos de desarrollo, seguridad y operaciones pueden incorporar la IA en sus procesos de DevOps.\n\n## 9 formas en que los equipos de DevSecOps utilizan la IA\n\n### 1. Hacer preguntas en la documentación mediante chatbots\nPara encontrar respuestas más rápido y reducir el cambio de contexto, los equipos de DevSecOps pueden usar chatbots con tecnología de IA para formular preguntas y obtener respuestas relevantes en tiempo real a partir de documentación u otros grandes volúmenes de texto. En lugar de abandonar el IDE o la plataforma donde están escribiendo e implementando código para buscar en la web, los desarrolladores pueden hacer una pregunta a un chatbot integrado y obtener una respuesta concisa sin interrumpir su flujo.\n\n### 2. Sugerir pruebas y generar archivos de prueba\nLos desarrolladores pueden usar la IA para sugerir pruebas y generar archivos de prueba para su código, directamente desde la solicitud de fusión. Esto puede ayudarlos a mejorar sus pruebas, asegurarse de tener una cobertura de prueba adecuada para sus cambios y reducir el tiempo que tienen que dedicar a escribir y pensar en las pruebas.\n\n### 3. Resumir los cambios del código\nAl realizar una confirmación o una solicitud de fusión, los desarrolladores pueden usar la IA para generar un resumen escrito de los cambios del código. Esto puede ayudarlos a ahorrar tiempo cuando confirman cambios y solicitan revisiones de código. La IA también puede ayudar a los revisores de código a ahorrar tiempo, y probablemente proporcionar una mejor revisión, al brindarles más contexto sobre los cambios realizados antes de que se sumerjan en el código.\n\n### 4. Obtener sugerencias sobre quién puede revisar el código\nLa revisión del código es un proceso importante, pero a veces frustrante y toma mucho tiempo, especialmente si no se recurre al revisor adecuado desde el principio.\n\nAl observar los cambios en el código y el gráfico de colaboraciones del proyecto, la IA puede sugerir automáticamente un revisor de código que pueda proporcionar comentarios más ágiles y de mejor calidad, así como detectar posibles problemas. La IA también puede ayudar a ahorrar tiempo al sugerir a otra persona que revise el código si un revisor sugerido no responde o si su revisión no es suficiente.\n\n### 5. Resumir las discusiones\nCuando las discusiones se vuelven largas o complicadas, los equipos pueden usar la IA para resumir todos los comentarios de un problema o ticket. Esto puede ayudar a todos a estar en sintonía y comprender de manera eficiente el estado de un proyecto y cuáles son los próximos pasos, lo que genera una colaboración más fluida y resultados más rápidos.\n\n### 6. Sugerir código\n[Las sugerencias de código con tecnología de IA](https://about.gitlab.com/blog/top-tips-for-efficient-ai-powered-code-suggestions-with-gitlab-duo/) pueden ayudar a los desarrolladores a escribir código de manera más eficiente al sugerir código directamente en su IDE mientras trabajan. Los desarrolladores pueden usar la IA para completar bloques de código, definir y generar lógica para declaraciones de funciones, generar pruebas de la unidad, sugerir código común como patrones de regex y mucho más. Estas funcionalidades definitivamente permiten a los desarrolladores ser más eficientes. Sin embargo, esta ganancia de eficiencia es mínima porque, [según nuestra investigación](https://about.gitlab.com/developer-survey/), dedican menos del 25 % de su tiempo a desarrollar código.\n\n### 7. Explicar cómo funciona un fragmento del código\nLos desarrolladores (o cualquier persona del equipo de DevOps) pueden usar la IA para obtener una explicación rápida de lo que hace un bloque de código y por qué se comporta de la manera en que lo hace, sin salir de su flujo de trabajo.\n\nUna explicación de código generada por IA puede ser particularmente útil para los desarrolladores que intentan comprender fragmentos de código que crearon otros profesionales o que están escritos en un lenguaje que no conocen bien. Además, según [nuestra investigación](https://about.gitlab.com/developer-survey/), los desarrolladores dedican el 13 % de su tiempo a comprender lo que hace el código, por lo que el ahorro de tiempo en este tipo de tareas es significativo.\n\n### 8. Resumir las vulnerabilidades en el código\nComprender una vulnerabilidad de seguridad recién detectada y cómo solucionarla no es algo trivial, pero las herramientas de seguridad con tecnología de IA pueden hacerlo más simple y más eficiente. Un [resumen generado por IA de una vulnerabilidad](https://about.gitlab.com/blog/developing-gitlab-duo-use-ai-to-remediate-security-vulnerabilities/) ayuda a los desarrolladores y profesionales de la seguridad a comprender la naturaleza de la vulnerabilidad, cómo podría explotarse y cómo corregirla. Algunas herramientas con tecnología de IA pueden incluso sugerir la mitigación de riesgos proponiendo ejemplos de código. Esto puede ser realmente útil para [ayudar a los equipos a evitar posibles amenazas y riesgos de seguridad](https://about.gitlab.com/the-source/ai/4-ways-ai-can-help-devops-teams-improve-security/) con menos esfuerzo.\n\n### 9. Pronosticar las métricas de productividad\nMediante el uso de la IA, los líderes de software pueden [pronosticar o predecir las métricas de productividad](https://about.gitlab.com/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai/), como la frecuencia de implementación, para identificar tendencias y anomalías a lo largo del ciclo de desarrollo de software. Estos conocimientos prácticos pueden ayudar a los equipos a implementar cambios para mejorar su eficiencia y los procesos de DevSecOps.\n\n## Los beneficios del uso de la IA en el desarrollo de software \nLos equipos de DevSecOps utilizan la IA, o planean utilizarla, para llevar a cabo muchas tareas, entre las que se incluyen:\n\n* Mejorar la eficiencia de su ciclo de vida de la entrega de software\n* Acelerar las duraciones de los ciclos\n* Optimizar las verificaciones de cumplimiento\n* Mejorar la productividad de los empleados\n* Mejorar el enfoque de seguridad\n* Mejorar la calidad del código \n* Mejorar la satisfacción del cliente \n* Mejorar la satisfacción de los empleados y la experiencia del desarrollador \n* Mejorar la colaboración entre los equipos \n* Mejorar el rendimiento de las aplicaciones \n* Automatizar las tareas repetitivas \n* Reducir los costos operativos \n* Reducir el cambio de contexto y la carga cognitiva \n* Reducir el error humano\n* Poner al día a los nuevos empleados más rápido\n* Ayudar a los empleados a [aprender nuevos lenguajes de programación](https://about.gitlab.com/blog/learn-advanced-rust-programming-with-a-little-help-from-ai-code-suggestions/)\n\n## Evitar problemas de privacidad y seguridad\nSi bien la integración de la IA en el proceso de desarrollo de software tiene muchos beneficios, es importante ser consciente de los riesgos potenciales, así como de los problemas y obstáculos habituales.\n\nSegún nuestra [investigación](https://about.gitlab.com/developer-survey/2024/ai/), la privacidad, la seguridad y la falta de familiaridad con las soluciones basadas en IA son obstáculos comunes que las personas encuestadas afirmaron haber encontrado o esperaban encontrar al implementar la IA en el ciclo de desarrollo de software. De todos los obstáculos identificados, las preocupaciones en torno a la privacidad y la seguridad de los datos fueron la respuesta más común (34 %), seguida de la falta de habilidades adecuadas (31 %) y la falta de conocimientos sobre IA (30 %).\n\nLos líderes empresariales deben garantizar que las implementaciones de IA respeten los estándares de privacidad y seguridad establecidos. Esto implica integrar mecanismos de control y validación de cumplimiento a lo largo del ciclo de vida de la IA para proteger los datos confidenciales y mantener la confianza del usuario. También es fundamental asegurarse de adoptar herramientas de IA que sean transparentes acerca de cómo sus modelos de aprendizaje automático utilizan los datos de su organización.\n\n## Descubra GitLab Duo\nTodas las funcionalidades que se mencionaron anteriormente, desde explicaciones del código hasta pruebas sugeridas, son parte de [GitLab Duo](https://about.gitlab.com/gitlab-duo/), el conjunto de funcionalidades de IA integradas en la plataforma de DevSecOps de GitLab. GitLab Duo ayuda a los equipos de DevSecOps a aumentar la eficiencia, reducir la duración de los ciclos y evitar el cambio de contexto con flujos de trabajo asistidos por IA en cada fase del ciclo de desarrollo de software, todo en una aplicación única.\n\n> Descubra por qué GitLab fue nombrado líder en el Magic Quadrant™ 2025 de Gartner® para asistentes de código de IA.\n> [Acceder al informe](https://about.gitlab.com/gartner-mq-ai-code-assistants/){class=\"button\" data-ga-name=\"gartner magic quadrant\" data-ga-location=\"thesource\"}","article","how-ai-helps-devsecops-teams-improve-productivity","content:es:the-source:ai:how-ai-helps-devsecops-teams-improve-productivity.yml","yaml","How Ai Helps Devsecops Teams Improve 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