#azure power platform

[haley-ai40]

Haley A.I. emerges as a versatile intelligent assistant platform poised to revolutionize how we interact with technology. Unlike singular-purpose assistants, Haley A.I. boasts a broader range of features, making it a valuable tool for individuals and businesses alike. This comprehensive exploration delves into the potential applications, functionalities, and future directions of this innovative AI solution.

Please try this product Haley A.I.

Unveiling the Capabilities of Haley A.I.

Haley A.I. leverages the power of machine learning, natural language processing (NLP), and potentially large language models (LLMs) to deliver a multifaceted experience. Here's a closer look at some of its core functionalities:

Conversational Interface: Haley A.I. facilitates natural language interaction, allowing users to communicate through text or voice commands. This intuitive interface simplifies interactions and eliminates the need for complex navigation or code.

Task Automation: Streamline repetitive tasks by delegating them to Haley A.I. It can schedule meetings, set reminders, manage calendars, and handle basic data entry, freeing up valuable time for users to focus on more strategic endeavors.

Information Retrieval: Harness the power of Haley A.I. to access and process information. Users can ask questions on various topics, and Haley A.I. will utilize its internal knowledge base or external sources to provide relevant and accurate answers.

Decision Support: Haley A.I. can analyze data and generate insights to assist users in making informed decisions. This can involve summarizing complex reports, presenting data visualizations, or identifying potential trends.

Personalized Assistant: Haley A.I. can be customized to cater to individual needs and preferences. By learning user behavior and collecting data, it can offer personalized recommendations, automate frequently performed tasks, and tailor its responses for a more optimal experience.

Integrations: Extend Haley A.I.'s capabilities by integrating it with existing tools and platforms. Users can connect Haley A.I. to their calendars, email clients, CRM systems, or productivity tools, creating a unified workflow hub. 

Harnessing the Power of Haley A.I. in Different Domains

The versatility of Haley A.I. makes it applicable across various domains. Let's explore some potential use cases:

Personal Assistant: Stay organized and manage your daily life with Haley A.I. Utilize it for scheduling appointments, setting reminders, managing grocery lists, or controlling smart home devices.

Customer Service: Businesses can leverage Haley A.I. to provide 24/7 customer support. It can answer frequently asked questions, troubleshoot basic issues, and even direct users to relevant resources.

Employee Productivity: Enhance employee productivity by automating routine tasks and providing real-time information retrieval. Imagine a sales representative being able to access customer data and product information seamlessly through Haley A.I.

Education and Learning: Haley A.I. can become a personalized learning assistant, providing students with explanations, summarizing complex topics, and even offering practice exercises tailored to their needs.

Data Analysis and Decision Making: Businesses can utilize Haley A.I. to analyze large datasets, generate reports, and identify trends. This valuable information can be used to make data-driven decisions and optimize strategies.

These examples showcase the diverse applications of Haley A.I. As the technology evolves and integrates with more platforms, the possibilities will continue to expand.

The Underlying Technology: A Peek Inside the Engine

While the specific details of Haley A.I.'s technology remain undisclosed, we can make some educated guesses based on its functionalities. Here are some potential components:

Machine Learning: Machine learning algorithms likely power Haley A.I.'s ability to learn and adapt to user behavior. This allows it to personalize responses, offer better recommendations, and improve its performance over time.

Natural Language Processing (NLP): The ability to understand and respond to natural language is crucial for a conversational interface. NLP techniques enable Haley A.I. to interpret user queries, translate them into machine-understandable code, and generate human-like responses.

Large Language Models (LLMs): These powerful AI models could play a role in Haley A.I.'s information retrieval and processing capabilities. LLMs can access and analyze vast amounts of data, allowing Haley A.I. to provide comprehensive answers to user inquiries.

The specific implementation of these technologies likely varies depending on Haley A.I.'s specific architecture and the desired functionalities. However, understanding these underlying principles sheds light on how Haley A.I. delivers its intelligent assistant experience.

Conclusion

HaleyA.I. emerges as a versatile and promising intelligent assistant platform. Its ability to automate tasks, access information, and personalize its responses positions it to revolutionize how we interact with technology. As the technology evolves and integrates with more platforms, the possibilities will continue to expand. By harnessing the power of AI responsibly and ethically, Haley A.I. has the potential to transform the way we work, learn, and live.

[trndigital01]

MS Power Platform | TrnDigital

Azure Power Platform - TrnDigital offers Microsoft Power Platform services like Power Apps, Power BI, Power Automate, Power Virtual Agents to Build, Analyse and Automate processes that empower you to drive your business with data. Contact Us !

[srinathpega]

Microsoft Dynamics 365 API Access token in Postman

Introduction Dynamics 365 Online exposes Web API endpoints, making integration simple. The most difficult part, though, is authenticating since Dynamics 365 Online uses OAuth2.0. Every HTTP request to the Web API requires a valid access bearer token that is issued by Microsoft Azure Active Directory. In this blog, I will talk about how to use Dynamics 365 Application User (Client ID and Secret…

[jcmarchi]

Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

New Post has been published on https://thedigitalinsider.com/datasets-matter-the-battle-between-open-and-closed-generative-ai-is-not-only-about-models-anymore/

Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

Two major open source datasets were released this week.

Created Using DALL-E

Next Week in The Sequence:

Edge 403: Our series about autonomous agents continues covering memory-based planning methods. The research behind the TravelPlanner benchmark for planning in LLMs and the impressive MemGPT framework for autonomous agents.

The Sequence Chat: A super cool interview with one of the engineers behind Azure OpenAI Service and Microsoft CoPilot.

Edge 404: We dive into Meta AI’s amazing research for predicting multiple tokens at the same time in LLMs.

You can subscribe to The Sequence below:

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

📝 Editorial: Datasets Matter: The Battle Between Open and Closed Generative AI is Not Only About Models Anymore

The battle between open and closed generative AI has been at the center of industry developments. From the very beginning, the focus has been on open vs. closed models, such as Mistral and Llama vs. GPT-4 and Claude. Less attention has been paid to other foundational aspects of the model lifecycle, such as the datasets used for training and fine-tuning. In fact, one of the limitations of the so-called open weight models is that they don’t disclose the training datasets and pipeline. What if we had high-quality open source datasets that rival those used to pretrain massive foundation models?

Open source datasets are one of the key aspects to unlocking innovation in generative AI. The costs required to build multi-trillion token datasets are completely prohibitive to most organizations. Leading AI labs, such as the Allen AI Institute, have been at the forefront of this idea, regularly open sourcing high-quality datasets such as the ones used for the Olmo model. Now it seems that they are getting some help.

This week, we saw two major efforts related to open source generative AI datasets. Hugging Face open-sourced FineWeb, a 44TB dataset of 15 trillion tokens derived from 96 CommonCrawl snapshots. Hugging Face also released FineWeb-Edu, a subset of FineWeb focused on educational value. But Hugging Face was not the only company actively releasing open source datasets. Complementing the FineWeb release, AI startup Zyphra released Zyda, a 1.3 trillion token dataset for language modeling. The construction of Zyda seems to have focused on a very meticulous filtering and deduplication process and shows remarkable performance compared to other datasets such as Dolma or RedefinedWeb.

High-quality open source datasets are paramount to enabling innovation in open generative models. Researchers using these datasets can now focus on pretraining pipelines and optimizations, while teams using those models for fine-tuning or inference can have a clearer way to explain outputs based on the composition of the dataset. The battle between open and closed generative AI is not just about models anymore.

🔎 ML Research

Extracting Concepts from GPT-4

OpenAI published a paper proposing an interpretability technique to understanding neural activity within LLMs. Specifically, the method uses k-sparse autoencoders to control sparsity which leads to more interpretable models —> Read more.

Transformer are SSMs

Researchers from Princeton University and Carnegie Mellon University published a paper outlining theoretical connections between transformers and SSMs. The paper also proposes a framework called state space duality and a new architecture called Mamba-2 which improves the performance over its predecessors by 2-8x —> Read more.

Believe or Not Believe LLMs

Google DeepMind published a paper proposing a technique to quantify uncertainty in LLM responses. The paper explores different sources of uncertainty such as lack of knowledge and randomness in order to quantify the reliability of an LLM output —> Read more.

CodecLM

Google Research published a paper introducing CodecLM, a framework for using synthetic data for LLM alignment in downstream tasks. CodecLM leverages LLMs like Gemini to encode seed intrstructions into the metadata and then decodes it into synthetic intstructions —> Read more.

TinyAgent

Researchers from UC Berkeley published a detailed blog post about TinyAgent, a function calling tuning method for small language models. TinyAgent aims to enable function calling LLMs that can run on mobile or IoT devices —> Read more.

Parrot

Researchers from Shanghai Jiao Tong University and Microsoft Research published a paper introducing Parrot, a framework for correlating multiple LLM requests. Parrot uses the concept of a Semantic Variable to annotate input/output variables in LLMs to enable the creation of a data pipeline with LLMs —> Read more.

🤖 Cool AI Tech Releases

FineWeb

HuggingFace open sourced FineWeb, a 15 trillion token dataset for LLM training —> Read more.

Stable Audion Open

Stability AI open source Stable Audio Open, its new generative audio model —> Read more.

Mistral Fine-Tune

Mistral open sourced mistral-finetune SDK and services for fine-tuning models programmatically —> Read more.

Zyda

Zyphra Technologies open sourced Zyda, a 1.3 trillion token dataset that powers the version of its Zamba models —> Read more.

🛠 Real World AI

Salesforce discusses their use of Amazon SageMaker in their Einstein platform —> Read more.

📡AI Radar

Cisco announced a $1B AI investment fund with some major positions in companies like Cohere, Mistral and Scale AI.

Cloudera acquired AI startup Verta.

Databricks acquired data management company Tabular.

Tektonic, raised $10 million to build generative agents for business operations —> Read more.

AI task management startup Hoop raised $5 million.

Galileo announced Luna, a family of evaluation foundation models.

Browserbase raised $6.5 million for its LLM browser-based automation platform.

AI artwork platform Exactly.ai raised $4.3 million.

Sirion acquired AI document management platform Eigen Technologies.

Asana added AI teammates to complement task management capabilities.

Eyebot raised $6 million for its AI-powered vision exams.

AI code base platform Greptile raised a $4 million seed round.

TheSequence is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

[sainacloudsoftwaresolutions]

"Part-1: Introduction to Integrating External Applications with Dynamics 365 for Operations: Step-by-Step Guide"

Welcome to our latest tutorial, where we'll guide you through the process of creating a new service method in Dynamics 365 for Operations and exposing it for external application usage. In this comprehensive tutorial, we'll demonstrate how to seamlessly integrate external applications with your Dynamics 365 environment, ensuring smooth data exchange and enhanced functionality.

[snakconsultancyservices]

Discover how our team's deep expertise in Microsoft Azure can help you build, deploy, and manage modern web apps, AI solutions, data services, and more

[manageditserviceslosangeles]

(AI) with Azure AI. Microsoft Azure’s comprehensive suite of AI services is paving the way for businesses to compete and thrive. Unlock the potential of AI with Azure AI’s diverse range of tools and services. Enhance decision-making, streamline operations, and discover new opportunities. Let’s not forget about Azure OpenAI, a cutting-edge collaboration between Microsoft and OpenAI, a renowned AI research lab. 

Start here to unravel the potential of Azure OpenAI for the best of both worlds. Harness the incredible language model of Azure Open AI, paired with the unbeatable scalability, security, and user-friendliness of the Azure platform. This dynamic partnership opens up endless business opportunities to revolutionize applications, enhance customer experiences, and ignite innovation. Keep reading to delve into the fundamentals of Azure AI and unlock a new realm of possibilities. 

GET STARTED WITH MICROSOFT AZURE AI

Table of Contents  hide 

1 What is Azure AI? What Services come under this?

1.1 What Is the Difference Between Azure and OpenAI?

1.2 What are the latest Azure AI features launched?

1.3 Addressing Challenges in Azure AI

1.3.1 What is there for IT Leaders?

1.4 How Can Azure AI Help Protecting and Building Data Insight for Your Business?

1.5 How can Industries Benefit from Azure AI?

1.5.1 Ready to Maximize Microsoft Azure?

1.5.2 How can you begin your journey with Azure AI?

1.5.3 Why choose ECF Data for the next-generation AI project?

[generateawareness]

[ibarrau]

[Fabric] ¿Por donde comienzo? OneLake intro

Microsoft viene causando gran revuelo desde sus lanzamientos en el evento MSBuild 2023. Las demos, videos, artículos y pruebas de concepto estan volando para conocer más y más en profundidad la plataforma.

Cada contenido que vamos encontrando nos cuenta sobre algun servicio o alguna feature, pero muchos me preguntaron "¿Por donde empiezo?" hay tantos nombres de servicios y tecnologías grandiosas que aturden un poco.

En este artículo vamos a introducirnos en el primer concepto para poder iniciar el camino para comprender a Fabric. Nos vamos a introducir en OneLake.

Si aún no conoces nada de Fabric te invito a pasar por mi post introductorio así te empapas un poco antes de comenzar.

Introducción

Para introducirnos en este nuevo mundo me gustaría comenzar aclarando que es necesaria una capacidad dedicada para usar Fabric. Hoy esto no es un problema para pruebas puesto que Microsoft liberó Fabric Trials que podemos activar en la configuración de inquilinos (tenant settings) de nuestro portal de administración.

Fabric se organiza separando contenido que podemos crear según servicios nombrados como focos de disciplinas o herramientas como PowerBi, Data Factory, Data Science, Data Engineering, etc. Estos son formas de organizar el contenido para visualizar lo que nos pertine en la diaria. Sin embargo, al final del día el proyecto que trabajamos esta en un workspace que tiene contenidos varios como: informes, conjuntos de datos, lakehouse, sql endpoints, notebooks, pipelines, etc.

Para poder comenzar a trabajar necesitaremos entender LakeHouse y OneLake.

Podemos pensar en OneLake como un storage único por organización. Esta única fuente de datos puede tener proyectos organizados por Workspaces. Los proyectos permiten crear sub lagos del único llamado LakeHouse. El contenido LakeHouse no es más que una porción de gran OneLake. Los LakeHouses combinan las funcionalidades analíticas basadas en SQL de un almacenamiento de datos relacional y la flexibilidad y escalabilidad de un Data Lake. La herramienta permite almacenar todos los formatos de archivos de datos conocidos y provee herramientas analíticas para leerlos. Veamos una imagen como referencia estructural:

Beneficios

Usan motores Spark y SQL para procesar datos a gran escala y admitir el aprendizaje automático o el análisis de modelado predictivo.

Los datos se organizan en schema-on-read format, lo que significa que se define el esquema según sea necesario en lugar de tener un esquema predefinido.

Admiten transacciones ACID (Atomicidad, Coherencia, Aislamiento, Durabilidad) a través de tablas con formato de Delta Lake para conseguir coherencia e integridad en los datos.

Crear un LakeHouse

Lo primero a utilizar para aprovechar Fabric es su OneLake. Sus ventajas y capacidades será aprovechadas si alojamos datos en LakeHouses. Al crear el componente nos encontramos con que tres componentes fueron creados en lugar de uno:

Lakehouse contiene los metadatos y la porción el almacenamiento storage del OneLake. Ahi encontraremos un esquema de archivos carpetas y datos de tabla para pre visualizar.

Dataset (default) es un modelo de datos que crea automáticamente y apunta a todas las tablas del LakeHouse. Se pueden crear informes de PowerBi a partir de este conjunto. La conexión establecida es DirectLake. Click aqui para conocer más de direct lake.

SQL Endpoint como su nombre lo indica es un punto para conectarnos con SQL. Podemos entrar por plataforma web o copiar sus datos para conectarnos con una herramienta externa. Corre Transact-SQL y las consultas a ejecutar son únicamente de lectura.

Lakehouse

Dentro de este contenido creado, vamos a visualizar dos separaciones principales.

Archivos: esta carpeta es lo más parecido a un Data Lake tradicional. Podemos crear subcarpetas y almacenar cualquier tipo de archivos. Podemos pensarlo como un filesystem para organizar todo tipo de archivos que querramos analizar. Aquellos archivos que sean de formato datos como parquet o csv, podrán ser visualizados con un simple click para ver una vista previa del contenido. Como muestra la imagen, aquí mismo podemos trabajar una arquitectura tradicional de medallón (Bronze, Silver, Gold). Aquí podemos validar que existe un único lakehouse analizando las propiedades de un archivo, si las abrimos nos encontraremos con un ABFS path como en otra tecnología Data Lake.

Tablas: este espacio vendría a representar un Spark Catalog, es decir un metastore de objetos de data relacionales como son las tablas o vistas de un motor de base de datos. Esta basado en formato de tablas DeltaLake que es open source. Delta nos permite definir un schema de tablas en nuestro lakehouse que podrá ser consultado con SQL. Aquí no hay subcarpetas. Aqui solo hay un Meta store tipo base de datos. De momento, es uno solo por LakeHouse.

Ahora que conocemos más sobre OneLake podemos iniciar nuestra expedición por Fabric. El siguiente paso sería la ingesta de datos. Podes continuar leyendo por varios lugares o esperar nuestro próximo post sobre eso :)

[biglisbonnews]

The future of business is here: How industries are unlocking AI innovation and greater value with the Microsoft Cloud Over the past six months, I have witnessed the staggering speed and scale of generative AI technology adoption, and how it has opened doors for organizations to imagine new ways to solve business, societal, and sustainability challenges. For many with modernized data estates fortified with the Microsoft Cloud, advanced AI technology is already unlocking innovation... The post The future of business is here: How industries are unlocking AI innovation and greater value with the Microsoft Cloud appeared first on The Official Microsoft Blog. https://blogs.microsoft.com/blog/2023/07/24/the-future-of-business-is-here-how-industries-are-unlocking-ai-innovation-and-greater-value-with-the-microsoft-cloud/

[code-life]

What is Microsoft Power Automate

In this article, I will know what is power automate, And Microsoft Power Automate, formerly known as Microsoft Flow, is a cloud-based service that allows users to create automated workflows across a wide range of applications and services. It is part of t

In this article, I will know what is power automate, And Microsoft Power Automate, formerly known as Microsoft Flow, is a cloud-based service that allows users to create automated workflows across a wide range of applications and services. It is part of the Microsoft Power Platform suite of business applications and is designed to help organizations automate routine tasks and processes. For more…

[srinathpega]

Power Platform User Permanently deletion from Azure Active Directory

Power Platform User Permanently deletion from Azure Active Directory

Introduction The methods of user deletion in Microsoft Power Platform from Microsoft 365 admin center, Azure Active Directory (Azure AD), and Power Platform admin center. In this article will learn the step-by-step process of the deletion of the user, This is in preview, the time of writing this blog. Delete users in Microsoft 365 admin center (Soft Delete) Go to Microsoft 365 admin center.…

[jcmarchi]

Best C# Testing Frameworks In 2024 - Technology Org

New Post has been published on https://thedigitalinsider.com/best-c-testing-frameworks-in-2024-technology-org/

Best C# Testing Frameworks In 2024 - Technology Org

Automation testing frameworks are essential in ensuring application performance and quality. C#  testing frameworks offer multiple features to meet various testing requirements. In this blog, we will explore the top C# testing frameworks of 2024.

Writing software code. Image credit: pxhere.com, CC0 Public Domain

C# testing Frameworks – Overview

The C# testing framework is a set of tools and an API that help construct, run, and manage the automation testing process in C# applications. Theses framework presents the developers with the systematic method to design and architect test suites so that the software works correctly and satisfies the given requirements.

C# testing frameworks typically offer features such as

Test case organization: Allow developers to group tests into logical units such as test classes or test suites for better organization and management.

Assertions: Build functions to state that the code has followed the desired sequence for the code under automation testing to make a program behave logically.

Setup and teardown: Support setup and teardown actions to correctly initialize the test environment before running tests and consequently clean up.

Test discovery and execution: Automatically execute and test the code and provide responses about test results and errors associated with the code.

Mocking and stubbing: Developers should be able to create mock objects to simulate dependencies and isolate units of code for automation testing.

Top C# Testing Frameworks In 2024

Let us see some of the top C# testing frameworks in 2024.

C# Selenium WebDriver

C# Selenium WebDriver is a framework for automation testing. It can process the navigation from the web page and detect its functions, performance, and user experience.

It also allows developers to write code and simulate user actions to verify elements on the web page. This allows for the creation of reliable automated tests that can be executed repeatedly to ensure the application’s behavior

Its cross-browser compatibility allows developers to write tests once and run them across multiple web browsers to ensure test coverage and compatibility with various user environments.

NUnit

The NUnit is a unit testing framework for languages like C# and VB.NET. It addresses the need for developers to write, manage, and run the unit test either within Visual Studio or through the command-line interface.

NUnit offers assertions, test runners, and attribute-based automation testing capabilities to validate the behavior of individual components. Its extensible architecture allows integration with various development tools and continuous integration pipelines that enable automation testing practices. NUnit supports parameterized tests, setup, teardown methods, and parallel test execution in automation testing. It remains the best framework for .NET developers to maintain code quality through unit testing.

MSTest

MSTest provides developers an efficient tool for writing and executing unit tests for .NET applications. MSTest can integrate with the IDE to create and manage unit tests effortlessly.

MSTest supports various testing features, such as test discovery, assertion methods, test execution, and result reporting, to effectively validate code’s behavior and functionality. It also offers attributes for defining test methods and classes to enhance the organization’s efficiency and maintainability.

It reduces the writing process and testing execution action and provides a wide user guide for most project types like .NET Core, .NET Framework, and Xamarin.

MSTest is integrated into the Microsoft Azure DevOps cloud platform to customize the unit cloud testing phase into automation testing and continuous feedback.

xUnit.NET

xUnit.NET follows the xUnit testing pattern, emphasizing simplicity, clarity, and extensibility. xUnit.NET provides developers a flexible platform for writing and executing unit tests to validate code functionality.

Its extensible architecture allows for easy integration with various development tools and frameworks. It also offers multiple assertion methods and test runners for a diverse set of testing scenarios.

xUnit.NET promotes test isolation, parallel test execution, and deterministic test outcomes. It supports test fixtures and setup/teardown methods. It can also encourage test-driven development (TDD) by integrating with popular IDEs. It also offers integration with continuous integration tools to incorporate unit testing into their CI/CD pipelines.

SpecFlow

SpecFlow is a BDD framework that uses natural language syntax for creating and writing scenarios, as well as the execution and management of acceptance tests for .NET software. It can be integrated with Visual Studio and other .NET development tools to enhance collaboration among developers and testers.

SpecFlow allows it to formulate executable specifications expressed in a human-comprehensible manner using the Gherkin syntax. These specifications can be added to the software documentation to maintain their functionality.

SpecFlow encourages collaboration and communication among cross-functional teams by defining a common language of application behavior expressed in a readable format. This approach also promotes code reusability and manageability by reusing the step definitions within many scenarios and features.

FluentAssertions

Fluent Assertions is the assertion library for .NET. It enables developers to write assertions in their unit test cases. It uses natural language that allows developers to identify assertions through the fluent interface.

It lets developers write assertion statements like natural language sentences to make the unit test easily understood. Such if-else statements held in the form of assertions can be written as “should” followed by a mentionable situation like “should be equal to” or “should contain,” showing what kind of behavior is expected for that tested code.

It supports various assertions like basic equality checks, collection assertions, and complex object comparisons. It also provides built-in support for asserting exceptions to verify that their code throws the expected exceptions under specific conditions. It also provides customizable assertion messages and failure descriptions.

Ranorex

Ranorex is an automation testing tool specially developed to make application testing of all platforms, including desktop, web, and mobile apps, easier and faster. Its graphical user interface (GUI) is so intuitive to create automated tests.

Unlike other testing tools, Ranorex has an object recognition capability that facilitates testers’ easy identification and interaction with UI elements, including buttons, text fields, and dropdown lists distributed across different platforms. This enables the development of automation testing, which precisely reproduces user interactions.

In addition, it provides built-in support for data-driven testing so they can easily write their test cases and execute them using different sets of data to ensure complete test coverage. It integrates with popular continuous integration and delivery tools that will automate the execution of the created tests as part of their build-up pipelines for continuous integration/delivery.

Its reporting capabilities offer a detailed assessment of the test results and common metrics needed. Testers can analyze the test results, identify problems, and track the progress of their testing activities by using customizable metrics.

BDDfy

BDDfy enables developers to implement Behavior-driven Driven Development practices in their .NET projects. BDDfy allows teams to focus on defining the behavior of their software through executable specifications written in natural language to establish collaboration between developers, testers, and stakeholders.

BDDfy also allows developers to write tests using natural language constructs to make the intent of the tests clear and understandable to all team members. This facilitates better communication and alignment of expectations throughout the development process.

The integration provides flexibility and versatility in test organization and execution, enabling teams to adopt BDD practices.

BDDfy provides detailed and insightful test reports that highlight the software’s behavior under test. These reports provide valuable documentation and can be shared with stakeholders to demonstrate progress and ensure alignment with requirements.

ApprovalTests

ApprovalTests is a versatile testing library designed to simplify verifying code output. ApprovalTests allows developers to approve the current behavior of their code by capturing and comparing its output against previously approved results.

Developers can quickly integrate ApprovalTests into their existing testing workflow regardless of the programming language or testing framework used. This makes it a valuable tool for various development environments like .NET, Java, Python, and more.

ApprovalTests improves handling complex output formats such as large data structures, images, and multi-line text. Developers can easily identify unexpected changes by capturing the code output and comparing it to approved results.

It effectively supports generating and managing approval files to review and update approved results as needed. This ensures that tests remain relevant and accurate over time.

NSubstitute

NSubstitute is a .NET mocking library constructed to simplify the process of creating and maintaining mock classes in unit testing. Mocking is a technique used in unit testing to simulate the behavior of dependencies in a component under test interactions with developers to isolate and test individual components.

NSubstitute expressive syntax enables developers to define mock objects and their behavior using natural language constructs. This makes it easy to understand and maintain mock setups.

NSubstitute supports various mocking scenarios and provides powerful features such as argument matches, callbacks, and received call verification to create flexible mock setups for unit tests.

The integration allows developers to use NSubstitute alongside their existing testing tools and practices without additional configuration.

NSpec

NSpec is a behavior-driven development testing framework for .NET developers designed to promote clarity, readability, and expressiveness in test specifications. It allows developers to write tests in a natural language format that closely resembles the software’s behavior specifications.

NSpec focuses on human-readable test specifications written using a syntax similar to plain English. This makes developers, testers, and stakeholders actively involved in the business and simplifies behavior definition and verification.

NSpec offers us features to do test management, such as grouping test cases under nested contexts, showing descriptive naming conventions, and a behavior-driven development paradigm. This allows developers to create clear and concise test specifications that accurately describe the expected behavior of the software under test. It also ensures compatibility and consistency across different testing environments, making adopting NSpec in existing projects easier.

Utilizing an automation testing framework tailored for C#, conducting automated testing for your website or mobile application becomes a straightforward task.

LambdaTest, an AI-powered test orchestration and execution platform, empowers you to execute manual and automated testing for your web projects on an extensive online browser farm featuring over 3000 real browsers, devices, and operating system configurations. Its cloud-based automation testing platform facilitates the execution of automation tests utilizing various C# testing frameworks such as Selenium, Appium, SpecFlow, NUnit, and others that help you test websites in different browsers.

Conclusion

In conclusion, C# testing frameworks in 2024 present developers with the right choices to meet various testing requirements. From NUnit’s strong focus on unit testing to SpecFlow’s emphasis on behavior-driven development, developers can access efficient tools for maintaining software quality. Whether the need is for unit testing or behavior-driven testing, these frameworks improve automation testing workflows and enhance the overall quality of C# applications.

[newkatzkafe2023]

Okay just saw friends, family and enemies, meeting Queen Kong, now they HAVE to meet female Godzilla and Godzilla Jr. XD

(emojis don't work for this platform.)

I CHALLENGE ANYONE WHO READS THIS POST, TO TAG AND REBLOG AS MANY OF YOUR FRIENDS AS YOU CAN!!!!!!!

#Scary wife Privileges😈

(Lmk Wukong) Ohhhhhhhhhh man everyone in megaspolis can see you the Legendary godzilla. Your husband begged for months for you and godji jr to meet his friends and we finally agreed. After all godzilla jr kids Needs friends his age, so later you got to pigsy's restaurant. And everybody was either amazed or terrified, Tang was the first to notice and well...😨

Tang: P-Pigsy I-I t-think godzilla is outside your r-restaurant😨😨😨😨

Pigsy: Very funny Tang😒😒😒

Sandy:(Upon seeing you) Oh dear....😥

Wukong: Hey, you guys I want you meet my wife Y/n or goddesszilla and my son JR!!!!🥰🥰

Then the others showed up, Mk was spazzing out in a good way as he ran around you in extreme excitement. Then Mei was over here, taking selfies with goji Jr and playing with the filers with him. And Redson called his parents about the possible danger. As he hid under a table in Pigsy's restaurant.

With their Enemies 🐂🕷🌬☠️🦁🐘🕊

the spider gang did not want any of that radiation smoke🕷🕷🕷

and neither did DBK and PIF🐂🌬

Jin and Yin were fanboying all over the place🩶🤎

The Mayor knew what was good for him and stayed back and far away along with Lady bone demon☠️💀

Nezha Wasn't sure if he should report this to heaven or not

And Lastly Azure and Peng were outraged because what else has Wukong hid from them. Meanwhile Yellowtusk Did everything in his power were not to piss us off.

As for Macaque he's muttering his Prayers as he literally digs his own grave😈🤣😰

(MR Wukong) Terror Pure unadulterated Terror, your husband finally decided to tell his master about you and Jr, Any well it really could have gone better. The ground shook the camp as they see Wukong and Fruity come back with a big body and soon an even bigger body. The pilgrims were pale as snow when they saw you and Jr.

Wukong: I wanted to tell you, that I'm married now and this is my wife Y/n and my other son godzilla Jr🤗

We stared down at the pilgrims, as the monk wasted no time passing out because the monkey king is Married to GODDESSZILLA!!! pigsy pissed himself and sandy actively ran away screaming. Meanwhile fruity and goji Jr because the fastest best friends😊🥰

(NR Wukong) Ohhhhhhhhhh man this is gonna be lit🤩🤩🤩🤩. Everyone was so stoked to meet godzilla, and Wukong Sorta found himself bragging about you and his Jr. Li was at a serious lost about what to do with this information, but Jr was quick to win him over with his surprisingly social nature.

Li:(getting Lick by Jr) haha awww your so cute🥰😊

Godzilla Jr:(purrs)

Su and the others had many questions for you which you Took the time to patiently answer to the best of your while Wukong cuddle and purred into you.

Meanwhile

Ao bing: Dad is that goddesszilla???😯😲🫨

Ao Guang: Whatever you do do not make eye contact😨😰😱

(HIB Wukong) Luier and Silly girl already loved you when you made their father happy. They also became fast friends and Siblings with Godzilla Jr. Goji Jr was quick to become very protective over his younger smaller brother and Sister. But overall the 3 were loving play mates, and Silly girl had started calling you mama at the end of the visit as Luier would sit by you asking questions. Which put another painful blush on his face as he watched the whole interaction. You also Might be wondering why pigsy didn't bother to flirt with you......????

Because both of you can definitely kill him😬

(Netflix Wukong) the dragon king Screamed like a little girl when he saw you at Lin's house. While Lin herself, she could even begin to comprehend what's going on here. Wukong had bought you and Goji Jr to meet his friends and so far Goji Jr loved Lin as he went and licked her face. Making Lin laugh at the tickles, meanwhile dragon king was down for the count as you stared down at him with a glare. Dragon king remained on his best behavior while you were there Because his life literally depended on it.

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