Open source AI for Playwright, Pillars of Continuous Testing and more
Summary
TLDREl episodio de 'Test Skill News Show' cubre novedades en soluciones de AI para pruebas, diferencias entre AI Ops y ML Ops, y pilar de prueba continua. Se presenta 'Apply Tools', una plataforma de validación AI, y tres características nuevas en 'Mabel Experience Conference'. Se discuten AI Ops y ML Ops, destacando su integración en sistemas cerrados. Se introduce 'Auto Playwright', un proyecto de código abierto que integra IA en flujos de trabajo de prueba. Se menciona un marco para prueba continua y herramientas como 'Spec to Test AI' y la experiencia de Kubernetes de Dynatrace, así como la plataforma DevSecOps de CloudB, enfocándose en la optimización y la seguridad en entornos de desarrollo.
Takeaways
- 🌟 La conferencia Mabel Experience presentó tres nuevas características: generación de IA auto-curativa, pruebas de carga de navegador y pruebas de móvil en beta privada.
- 🤖 La diferencia entre AI Ops y ML Ops se aclaró, destacando cómo AI Ops mejora las operaciones IT y ML Ops se enfoca en la implementación y monitoreo de modelos de aprendizaje automático.
- 🚀 Se introdujo Auto Playwright, una solución de código abierto que integra la inteligencia artificial en el flujo de trabajo de pruebas con Playwright.
- 📝 Se destacó la importancia de los cuatro pilares para la prueba continua: prueba focalizada, resultados informativos, confiabilidad y procesos repetibles.
- 🔍 Se mencionó Spec to Test AI, una herramienta de gestión de proyectos que utiliza IA para mejorar la visibilidad y el análisis de requisitos.
- 🛠️ Se anunció una nueva experiencia de Kubernetes por parte de Dynatrace, con capacidades avanzadas de observabilidad, seguridad, AI y automatización.
- 🌐 CloudB lanzó una plataforma nativa en la nube para DevSecOps, impulsada por Amazon Elastic Kubernetes Services, para redefiner los estándares de DevSecOps en la nube.
- 🔗 Se invitó a los espectadores a visitar los enlaces proporcionados para obtener más información sobre las herramientas y soluciones mencionadas en el programa.
- 🎁 Se promocionó la oferta de cuenta gratuita de Applause Tools para mejorar las pruebas de automatización utilizando IA visual.
- 📢 El presentador, Joe, enfatizó su misión de ayudar a los espectadores a crear pipelines de automatización de pila completa y de extremo a extremo.
Q & A
¿Qué es la plataforma de validación AI visual que se menciona en el guion y cómo puede ayudar a los proyectos de animación?
-La plataforma de validación AI visual mencionada es Apply Tools. Se describe como un cambio de juego que permite a los usuarios probarla gratuitamente creando una cuenta con un enlace especial en los comentarios. Ayuda a llevar proyectos de animación al siguiente nivel mediante la validación AI visual.
¿Cuáles son las tres nuevas funciones presentadas en la conferencia Mabel Experience?
-Las tres nuevas funciones son: 1) Auto healing impulsado por inteligencia artificial, que mejora las capacidades existentes y reduce el mantenimiento de pruebas en un 95%. 2) Pruebas de carga de navegador, que simplifican las pruebas de rendimiento. 3) Pruebas móviles, actualmente en beta privada, que ofrecen una cobertura de pruebas automatizadas para aplicaciones Android e iOS.
¿Qué diferencia hay entre AI Ops y ML Ops según el artículo mencionado en el guion?
-AI Ops se centra en mejorar y automatizar operaciones de TI utilizando IA y aprendizaje automático, ofreciendo soluciones proactivas y optimización de costos. Mientras tanto, ML Ops se enfoca en la implementación, monitoreo y gestión de modelos de aprendizaje automático en producción, asegurando su reproducibilidad y escalabilidad.
¿Qué es Auto Playwright y cómo integra la inteligencia artificial en el flujo de trabajo de pruebas?
-Auto Playwright es un proyecto de código abierto que integra la inteligencia artificial en el flujo de trabajo de pruebas. Permite crear pruebas rápidamente utilizando texto plano sencillo y traduce instrucciones en texto plano en comandos de prueba reales, simplificando el proceso de ejecución de pruebas con Playwright.
¿Cuáles son las cuatro pilares para el testing continuo según el artículo de BOS mencionado en el guion?
-Los cuatro pilares son: 1) Pruebas focalizadas, que deben apuntar a los componentes y capas adecuados de la aplicación. 2) Resultados informativos, que proporcionan retroalimentación clara y útil. 3) Confiabilidad, donde las pruebas deben reflejar con precisión el estado del software. 4) Procesos repetibles, con estrategias sólidas para datos de prueba y entornos.
¿Qué herramienta de gestión de proyectos se menciona en el guion y cómo ayuda a superar las limitaciones de las herramientas tradicionales?
-Se menciona Spec to Test AI, una herramienta que supera las limitaciones de herramientas tradicionales como JIRA. Ayuda con el análisis de requisitos, optimización de tareas, generación de casos de prueba y análisis de riesgos de ciberseguridad, ofreciendo una aproximación más inteligente y completa a la gestión de proyectos.
¿Qué anuncio hizo Dynatrace y cómo afecta a la experiencia de Kubernetes para los equipos de ingeniería de plataformas?
-Dynatrace anunció una experiencia de Kubernetes innovadora para equipos de ingeniería de plataformas. Esta experiencia mejora la gestión y optimización de entornos de Kubernetes con capacidades de AI avanzadas en observabilidad, seguridad y automatización, incluyendo mantenimiento predictivo que detecta y pronostica anomalías en los clústeres de Kubernetes.
¿Qué plataforma de DevSecOps nativa en la nube anunció CloudB y qué ventajas ofrece?
-CloudB anunció el lanzamiento de su nueva plataforma de DevSecOps nativa en la nube, impulsada por Amazon Elastic Kubernetes Services. Esta plataforma ofrece integración sin problemas con AWS, capacidad de escalar, medidas de seguridad proactivas, y simplificación de la gestión de flujos de trabajo.
¿Qué es el modelo de Auto healing y cómo contribuye a la reducción del mantenimiento de pruebas?
-El modelo de Auto healing es una característica que utiliza un modelo de lenguaje grande para mejorar las capacidades de curación automática existentes, lo que ayuda a reducir el mantenimiento de pruebas en un 95% y a aumentar significativamente la productividad.
¿Cómo se describe el uso de la inteligencia artificial en el campo de las pruebas de carga de navegador?
-La inteligencia artificial se utiliza para simplificar las pruebas de rendimiento, permitiendo a los equipos convertir pruebas de navegador y API de bajo código en pruebas de rendimiento, asegurando que el rendimiento de la aplicación se alinee con la experiencia del usuario real.
¿Qué beneficios se mencionan al integrar AI Ops y ML Ops en un sistema cerrado?
-La integración de AI Ops y ML Ops puede crear sistemas cerrados donde las insights impulsadas por IA de AI Ops informan y automatizan acciones dentro de ML Ops, trabajando juntos para mejorar la eficiencia y la toma de decisiones en operaciones de datos e inteligencia artificial.
Outlines
😀 Introducción a soluciones AI y testing
El primer párrafo introduce una nueva solución de código abierto que integra AI en el testing, y diferencia entre AI Ops y ML Ops. Se menciona una plataforma de validación AI visual llamada 'Apply Tools', y se anuncian tres características nuevas en la conferencia Mabel Experience: Auto healing impulsado por IA, testing de carga de navegador y testing móvil en beta privada. Además, se discuten los beneficios de AI Ops y ML Ops y cómo pueden integrarse para mejorar operaciones y modelos de aprendizaje automático.
😉 Herramientas y marcos para testing continuo
El segundo párrafo presenta un marco para el testing continuo basado en un artículo de BOS, que aborda cuatro pilares clave: testing enfocado, resultados informativos, confiabilidad y procesos repetibles. Se discute un nuevo herramienta llamada 'Spec to Test AI' que ofrece una aproximación inteligente al análisis de requisitos y gestión de proyectos, y se menciona la integración de IA para mejorar la seguridad cibernética y la generación de casos de prueba. Finalmente, se anuncian dos plataformas DevSecOps: una experiencia de Kubernetes de Dynatrace y una plataforma nativa de la nube de CloudB, ambas enfocadas en agilidad, seguridad y optimización de experiencias.
Mindmap
Keywords
💡AI Ops
💡ML Ops
💡Auto healing
💡Pruebas de carga de navegador
💡Pruebas móviles
💡Autoplaywright
💡Pruebas continuas
💡Specto Test AI
💡DevSecOps
💡CloudB
Highlights
New open source solution integrating AI into testing workflows.
Differences between AI Ops and ML Ops explained.
Introduction of three new features at the Mabel experience conference: generative AI, browser load testing, and mobile testing.
Generative AI can reduce test maintenance by 95% and boost productivity.
Browser load testing simplifies performance testing, converting existing tests into performance tests.
Mobile testing in private beta offers automated test coverage for Android and iOS apps.
AI Ops and ML Ops serve distinct roles in IT and data operations, with AI Ops focusing on proactive problem resolution and ML Ops on machine learning model management.
Integration of AI Ops and ML Ops can create closed-loop systems for automated actions within MLOps.
Open source project Auto Playwright integrates AI into testing workflows using simple plain text instructions.
Auto Playwright leverages open AI technology for intuitive and effective test automation.
Framework for enhancing continuous testing in CI/CD with four pillars: Focus testing, informative results, trustworthiness, and repeatable processes.
Spec to Test AI as a project management invisibility tool offering comprehensive and intelligent project visibility and management.
Spec to Test AI's features include requirement analysis, AI-assisted task optimization, and automatic test case generation.
Din Trace's new Kubernetes experience for platform engineering teams with advanced observability, security AI, and automation capabilities.
CloudB's new cloud-native DevSecOps platform powered by Amazon Elastic Kubernetes Services for seamless AWS integration and scalability.
CloudB platform's benefits include proactive security measures, simplified workflow management, and continuous innovation support.
Links to valuable resources and sponsor offers provided in the first comment of the video.
Transcripts
want to know a new open source solution
that brings AI to play right what's the
difference between AI Ops versus ml Ops
and what are some pillars to continuous
testing find out in this episode of the
test skill news show for the week of
November 12th so grab your favorite cup
of coffee a tea and let's do this but
first are you looking to take your
animation projects to the next level
look no further than apply tools in the
visual AI validation platform trust me
it is a game changer plus you can try it
out for yourself by creating a free
account now by using the special link in
the comment down below and see the
difference for yourself so at the annual
Mabel experience conference they
unveiled three new features the first is
really no surprise it's generative AI
powered Auto healing and this feature
enhances Mabel's existing Auto healing
capabilities using a large language
model to help reduce test maintenance by
95% and significantly boost productivity
the second new feature was browser load
testing and this innovation simplifies
performance testing allowing teams to
convert low code browser and API tests
into performance tests ensuring app
performance aligns an actual user
experience this is a growing Trend we
seeing more and more companies invest in
this area for performance testing using
real browsers and leveraging existing
automated tests as performance test and
the third Innovation is mobile testing
So currently in a private beta and this
feature offers comprehensive and
reliable automated test coverage for
Android and iOS apps and enabling faster
test creation and execution so you
probably heard a lot about AI Ops and ml
Ops but you may not be sure of the
differences while I have a resource for
you that goes over what the two are and
what the key differences are and
benefits of both so I actually found
this post on LinkedIn by Scott Moore
posting to this article so thank you
Scott for letting me know about this
resource and the article itself is what
actually dives into AI Ops and mlops and
how they're often confused but they
serve distinct and critical roles when
you're using them within it and data
operations and this blog post sheds
light on the differences between the two
and approaches of their unique
application so and it goes into detail
how AI Ops applies Ai and machine
learning to enhance and automate it
operations and some of the benefits it
offers is proactive problem resolutions
automation of routine task and enhanced
visibility of it infrastructure reduced
downtime and cost optimization and it
also then Compares it to mlops which
which is really streamlining machine
learning which focuses on deployment
monitoring and management of machine
learning models in production and some
of its benefits is to help you ensure
reproducibility
scalability uh governance and
reliability of machine learning models
and it also concludes by saying while AI
Ops and mlops are different they are not
mutually exclusive and their integration
can create closed loop systems where AID
driven insights from AI Ops informs and
automates actions within mlops so they
actually work together all right pretty
much every time we talk about AI with
automation tooling on this show it has
to do with vendor-based uh Solutions
well I found a open source solution that
just came out it's in its infancy called
Auto playwright which is an open source
project that integrates artificial
intelligence into your testing workflow
with playwright let's check it out so I
first heard about this on a comment on
my LinkedIn post by Luke who goes hey I
may find this interesting I clicked on
the link and said wow I know you all
definitely would find this interesting
and he also goes over how you definitely
want to check out some other things he
talked about on Reddit that explains it
a little more in detail and the link to
GitHub goes over how you can use
automating playwright steps using chat
GPT and how it really streamlines your
testing workflow and simplifies the
process of running playwright test using
AI making it more intuitive and
effective and so some key features of
this particular solution is ease of use
so autop play right allows for rapidly
creating tests using simple plain text
AI J prompts you can also use this tool
to leverage open AIS technology to
translate plain text instructions into
actual testing commands and auto
playright can handle various tasks from
clicking links to performing complex
queries and assertions and like I said
this is really brand new and I think Luc
is looking for input so definitely give
you input in the comment down below or
I'll have him tagged within the post
post as well so let them know features
you'd like to see or improvements that
could be made to Auto playright it's a
great step forward for folks that are
looking for open source solutions that
actually integrate AI so thank you Luke
for this new tool for the community
appreciate it are you looking for ways
to enhance your continuous testing in
your cicd well I have a framework that
can help you do this I actually came
across this article on my LinkedIn
YouTube feed it was a post by Amala that
caught my attention how she talked about
how this post by BOS helped her really
realize the potential of her continuous
testing efforts and she links to bz's
article that he recently updated on
supporting continuous testing with
fighter or fitr test Automation and this
model goes over Focus testing which is
automated tests must Target the right
application components and layers
ensuring the efficient and relevant the
second pillar is informative results
test should provide clear actable
feedback tailored to different audiences
from developer to managers the third is
trustworthiness reliability is key test
must accurately reflect the state of the
software avoiding false positives and
negatives and the fourth pillar is
repeatable processes so test should be
able to run on demand necessitating
robust strategies for test data
environments and it goes over some of
the challenges and solutions such as
different hurdles that you can use or to
get over when you're doing continuous
testing definitely a must read article
by BOS that you should check out and
that first comment down below I also
came across a new tool I haven't heard
of before from Scott on
LinkedIn and he talks about a project
management invisibility tool that they
came out called spec to test Ai and this
solution is a platform that transcends
the limitations of traditional project
management tools like jera offering a
comprehensive and intelligent approach
to project visibility and management I
know a lot of testers and developers
struggle with with current Solutions and
this article goes into detail on how
spec to test AI helps with Comprehensive
requirement analysis like unlike
traditional platforms that simply create
catalog user stories spec to test AI
conducts an in-depth examination of
requirements across five categories
uring Clarity and alignment with
business goals it also leverages AI to
analyze requirements and minutes a task
typically takes usually over an hour
providing feedback and optimization it
also helps with cyber security risk it a
needs in developing robust security
requirements and test cases from the
start it also has automatically test
case generation which generates
prioritize test cases that address
functionality complexity and
interdependencies with minimal user
intervention so if you haven't checked
this out if this is tool us sounds
useful for you definitely check it out
and let me know about it in the comments
down below and this announcement goes
over how din trce announced a
groundbreaking kubernetes experience for
platform engineering teams and this new
offering empowered
din traces Advanced observability
security Ai and automation capabilities
and it's helped set to transform how
kuet environments are managed and
optimized and it also has predictive
maintenance with AI leveraging din
traces casual and predictive AI the
platform could automatically detect and
forecast anomalies in your kuber
clusters and this can Empower you to
proactively address issues preventing
negative impacts on user experience and
the new experience also supports key Dev
SEC Ops processes including automated
quality Gates and validation of builds
deployments and releases and this really
ensures a reliable secure and scalable
kubernetes environment optimizing
developer experience and testers
experience and if you're looking for a
devs Ops platform I have another
announcement and this one is from Cloud
B and Cloud B announced the launch of
its new Cloud native Dev SEC Ops
platform and this platform is powered by
Amazon elastic kubernetes services and
it sets to redefine the standards of Dev
SEC Ops in the cloud and some of the key
benefits of this platform is it has
seamless AWS integration it offers the
ability to scale up or down
transparently ensuring both cost
Effectiveness and performance
optimization it allows for proactive
security measures it helps simplify
workflow management and this is really a
trend I see a lot more companies trying
to bring companies more agility security
and versus utility to Enterprises enable
them to innovate continuously in an
application and experience first world
and these are just two platforms that
can help you do that and you can check
them all in that first link down below
and for links of everything of value we
covered in this news episode head on
over to Links in that first comment down
below and while you're there make sure
to check out our awesome sponsor Apple
tools free account offer and discover
how to take your automation testing to
the next level leveraging visual AI so
that's it for this episode of the test
skill new show I'm Joe my mission is
help you succeed in creating end to- end
full stack pipeline automation
awesomeness as always test everything
and keep the good cheers
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