#api testing services

[robotico-digital]

Exploring the Power of Artificial Intelligence in API Testing Services

As software ecosystems become more interconnected, the complexity of APIs will only increase. Relying on traditional testing strategies is no longer sufficient. By combining the precision of automation with the intelligence of AI, API Testing Services become faster, smarter, and more secure. At Robotico Digital, we empower businesses with future-proof API testing that not only ensures functionality and performance but also embeds intelligent Security testing API protocols to guard against ever-evolving threats. Let us help you build trust into your technology—one API at a time.

[testrigtechnologies]

Introduction to Mobile Application Testing

In today’s mobile-first world, the success of an app hinges on its performance, functionality, and user experience across a wide range of devices and platforms. As organizations strive to meet rising user expectations and deliver flawless mobile experiences, mobile application testing has become an indispensable part of the development lifecycle.

This article is the first installment in our comprehensive blog series on Mobile Application Testing, where we’ll guide you through everything from foundational concepts to advanced testing strategies. In this post, we’ll cover what mobile application testing is, why it's critical, how it differs from web testing, and where it's applied in the real world.

What is Mobile Application Testing?

Mobile application testing refers to the process of testing apps developed for mobile devices—be it smartphones or tablets—under various conditions to ensure they meet quality standards. The testing scope includes verifying app functionality, usability, security, and performance across multiple devices, operating systems (like Android and iOS), and network environments.

This testing ensures that users have a seamless experience, regardless of the device or conditions they use.

Categories of Mobile Apps:

Native Apps: Built specifically for a platform (e.g., Swift for iOS, Kotlin for Android).

Hybrid Apps: Developed using web technologies and wrapped in a native container.

Mobile Web Apps: Accessed via mobile browsers and resemble responsive websites.

Each app type requires unique testing approaches to validate their behavior across platforms and devices.

Why Mobile App Testing Is Important

Understanding the mobile app testing importance helps teams identify risks early, improve product quality, and meet business goals. Here are the top reasons it matters:

1. Diverse Devices and OS Versions

Android alone has thousands of devices with different specifications. Testing ensures consistent behavior across a fragmented ecosystem.

2. User Expectations Are Higher Than Ever

Mobile users expect fast, responsive, and bug-free apps. A single crash or lag can lead to uninstalls or negative reviews.

3. Rapid Development Cycles

Agile and CI/CD workflows demand frequent updates. Testing ensures that each release maintains the same level of quality.

4. App Store Compliance

Both Google Play and Apple App Store have strict approval criteria. Failing them can delay launches or reduce visibility.

5. Business Continuity

For many brands, mobile apps are the primary revenue or engagement channels. A poor-quality app can result in lost customers and damaged brand credibility.

Real-World Use Cases of Mobile Testing

✅ Retail & E-commerce

Apps must handle peak loads, secure payments, and responsive UIs to deliver excellent shopping experiences.

✅ Banking & FinTech

Security testing, regulatory compliance, and multi-factor authentication are essential.

✅ Healthcare Apps

Accuracy, privacy, and reliability are crucial—especially when handling patient data.

✅ Transport & Mobility

Apps like Uber or Lyft require flawless GPS, maps integration, and real-time updates.

✅ Education Platforms

Multimedia support, language compatibility, and accessibility testing are key for eLearning apps.

Final Thoughts

So, what is mobile application testing really about? It's about ensuring your mobile app performs flawlessly in real-world conditions, on real devices, under real constraints. And why is mobile app testing important? Because quality matters—and users won’t settle for anything less.

This blog sets the stage for the rest of our Mobile Testing Series, where we’ll take a deeper look into real device vs. emulator testing, types of mobile testing, common challenges, automation tools, and agile testing strategies.

For more information, get in touch with the leading Mobile application testing company

[solutionmindfire]

APIs (Application Programming Interfaces) are the backbone of modern digital ecosystems. They enable seamless interaction between applications, platforms, and services. However, their exposure makes them a prime attack vector. API security testing identifies vulnerabilities in APIs to ensure data confidentiality, integrity, and availability.

[apisolutionsn2nsystems]

Integrating Mobile Recharge API Services into your recharge business is a strategic move that enhances customer satisfaction, expands network accessibility, enhances customer engagement, diversified revenue streams, and provides a competitive edge. These services offer instant recharges, wide network accessibility, enhanced security measures, scalability and flexibility, and a customer-centric approach. By offering these services, businesses can stay adaptable to market trends, meet customer expectations, and provide an unparalleled customer experience.

[mindfiresolutions-blog]

Top Machine Learning Libraries In Java

Java is the only general purpose programming language which finds wide use by developers for building secure enterprise grade applications, desktop applications, web applications, and mobile apps. Java 9 further helps developers to build applications for both large and small devices by providing a number of new features – a new module system, a new command line tool, and several updated APIs. At the same time, Java is currently one of the most popular programming languages for machine learning.

A large percentage of data scientists and machine learning developers prefer Java to other programming languages while improving network security, protect cyber attacks, and detect frauds. The language features available in Java makes it easier for programmers to write machine learning algorithms. The developers can accelerate custom machine learning application development by taking advantage of the Machine Learning libraries In Java.

Brief Overview of 10 Robust Machine Learning Libraries In Java

1) Java Machine Learning Library (Java-ML)

Java-ML is designed as a collection of machine learning algorithms. It even provides interface for various types of machine learning algorithms. By design, the library is a clear interface instead of graphical user interface. Hence, only skilled Java programmers and developers are able to use it . They have option to learn Java-ML by referring to its well-documented source code as well as tutorials and code samples.

2) Java Statistical Analysis Tool (JSAT)

The Java library for machine learning was developed by Edward Raff for self-education. JSAT still provides implementation of standard machine learning algorithms in pure Java. The developers can even use JSAT as a lightweight Java library without external dependencies. But they cannot use the library to solve complex machine learning project. JSAT still helps machine learning developers to sole small to medium size problems quickly.

3) Waikato Environment for Knowledge Analysis (Weka)

The machine learning algorithms provided by Weka helps developers to simplify a variety of data mining tasks. Weka even provides a number of tools for data pre-processing, classification, clustering, regression, and visualization. The developers even have option to use the machine learning algorithms provided by Weka to a dataset directly or call the algorithms from Java code. At the same time, the developers can also use Weka to create new machine learning schemas without putting extra time and effort.

4)  Konstanz Information Miner (KNIME)

KNIME was originally an analytics and reporting library. But KNIME is currently one of the most popular build software for advanced data science. The tools provided by KNIME help users to discover new potential hidden in data, mine data for fresh insights, and predict new future. The data scientists can use KNIME to integrate different types of data collected from various sources to widely used tools. At the same time, the software developers can use KNIME to connect applications to data sources by creating custom connectors, implement new algorithms, and create new data visualization.

5) Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI)

The open source data mining software is written in Java programming language. But it is compiled with Python and Maven. Despite being designed as research software, ELKI is designed based on extensions. It allows developers to use algorithms, indexes, visualization, data types, and distance functions as extensions. However, ELKI allows developers to keep the data management tasks and data mining algorithms separated. The separation further makes it easier for programmers to evaluate data mining algorithms and data management tasks independently.

6) RapidMiner

The commercial data science platform is currently being used by large enterprises like Samsung, GE, SalesForce, Cisco, Hitachi and Siemens. RapidMiner comes with a set of features and tools to simplify various tasks performed by data scientists. It even uses automated machine learning to speed up and simplify various data science projects. The data scientists can use RapidMiner Studio to create visual workflow, RapidMiner Server to simplify model deployment and management, and RapidMiner Radoop to implement code free data science.

7) Massive Online Analysis (MOA)

The widely used data stream mining framework comes with a number of machine learning algorithms and tools to evaluate the algorithms. While using MOA, developers can avail a variety of machine learning algorithms – classification, clustering, regression, concept drift detection, outlier detection, and recommender system. The developers can use MOA to perform real-time big data streaming and large scale machine learning. They even have option to extend and scale the Java-based framework to meet complex project needs.

8) Eclipse Deeplearning4j

Deeplearning4j is a Java based platform. The distributed deep learning library is compatible with a number of JVM-based programming languages – Kotlin, Scala and Clojure. Also, Deeplearning4j finds extensive use as a scalable and open source distribution library in varied business environments on distributed CPUs and GPUs. It even features micro-service architecture and takes advantage of a robust distributed computing framework like Hadoop. The developers can even avail the tools provided by Deeplearning4j to perform machine learning ETL operations, evaluate machine learning algorithms, and integrate Java and native C++.

9) Mallet

Mallet is designed as a Java-based package for a variety of machine learning applications to text. The sophisticated tools available in Mallet make it easier for developers to simplify document classification, sequence tagging, topic modelling, and numerical optimization. At the same time, Mallet transforms text documents into numerical representation efficiently and flexibly through a system of pipes. The user can even extend Mallet through add-on packages to meet complex project needs.

10) Encog Machine Learning Framework

In addition to supporting advanced machine learning algorithms, Encog also supports classes for data normalization and processing. The framework further provides multi-threaded training algorithms that can scale according to multicore hardware. Encog makes it easier for programmers to model and train machine learning algorithms by providing a GUI-based workbench. It supports an array of standard machine learning algorithms including neural networks, genetic programming, Bayesian networks, hidden Markov models, and support vector machine.

On the whole, the Java developers have option to choose from a wide range of Java libraries for machine learning. Some of these libraries are machine learning development platforms, whereas others provide a collection of machine learning algorithms. Hence, the developers must keep in mind the precise needs of each project while comparing these widely used machine learning libraries for Java programming language.

[ameliajonas24]

API Testing: Ensuring Quality and Reliability in the Digital Era

Introduction:In today's fast-paced digital era, where applications and services rely heavily on seamless integration and data exchange, the role of APIs (Application Programming Interfaces) has become increasingly vital. Modern software systems rely heavily on APIs to facilitate communication and interaction across various platforms, services, and applications. However, extensive testing is necessary to guarantee the dependability and quality of these APIs. We will discuss the importance of API testing services, their advantages, and best practices in this blog to guarantee reliable and effective API implementations.

API Testing Service: Enhancing Quality and Reliability API testing plays a critical role in identifying and resolving issues related to functionality, performance, security, and compatibility. By conducting comprehensive API testing, organizations can mitigate risks, enhance the end-user experience, and maintain the integrity of their software systems.

Test Code: What it Should Do When performing API testing, one of the key aspects is to verify whether the API functions as intended. This involves testing the code to ensure that it behaves correctly and produces the expected results. By thoroughly testing the code, developers and QA testers can identify any discrepancies or bugs and rectify them before deployment.

What to Test For: Look for the Ordinary In API testing, it is crucial to test for both normal and edge cases. Normal cases involve testing the API under typical usage scenarios to ensure that it handles requests, processes data accurately, and returns the expected responses. By examining ordinary use cases, organizations can ensure that their APIs are reliable and perform optimally during regular operations.

What To Test For: Stressing the System APIs must be capable of handling high loads and stress conditions without compromising performance or stability. Stress testing involves subjecting the API to heavy traffic, concurrent requests, and large data volumes to assess its behavior under such conditions. By conducting stress tests, organizations can identify potential bottlenecks, performance issues, and scalability limitations, allowing them to optimize their APIs accordingly.

Continue Reading...

[manishasg]

How to hire an API developer for business

Unlock the potential of your project by hiring top-notch API developers! 🚀

Discover the key steps to assemble a stellar API development team and bring your vision to life. From technical expertise to collaboration skills, find the perfect match for your project's success.

Learn more: https://www.fortunesoftit.com/sg/hire-api-developers-singapore/

[vgdtechnologies]

Manual QA Essentials: A Practical Guide to Tools for Successful Testing

In the dynamic landscape of software development, manual quality assurance (QA) testing plays a pivotal role in delivering reliable and user-friendly applications. This step-by-step guide will walk you through the process of conducting effective manual QA testing using essential software tools to ensure the highest level of software quality. Get More Details.

[keploy12]

Harnessing the Power of Open Source API Testing

Introduction

In the ever-evolving landscape of software development, ensuring the reliability and functionality of APIs (Application Programming Interfaces) is crucial. APIs serve as the bridge between different software components, enabling them to communicate and interact seamlessly. To ensure that these APIs perform as expected, API testing is essential. Open source API testing tools have gained popularity as cost-effective and flexible solutions for developers and testers alike. In this article, we'll explore the significance of open source API testing, its benefits, and some of the most popular tools available today.

The Importance of API Testing

APIs are the backbone of modern software applications. They facilitate data exchange, allow different systems to work together, and enable third-party integrations. Ensuring the quality and reliability of APIs is paramount to avoid system failures, security breaches, and poor user experiences. Here are some key reasons why API testing is crucial:

Functionality Testing: API testing verifies that the API performs its intended functions correctly. It ensures that the data inputs and outputs are accurate and adhere to the API's specifications.

Security: Security vulnerabilities in APIs can have severe consequences. API testing helps identify and mitigate potential security risks, such as unauthorized access or data leaks.

Performance: Poorly performing APIs can slow down applications or even lead to outages. API testing assesses an API's performance under various conditions to ensure it can handle expected levels of traffic.

Compatibility: APIs are used across different platforms and devices. Testing ensures that the API works consistently across various environments and devices.

Documentation: Testing helps validate and improve API documentation, making it easier for developers to understand and use the API.

Open Source API Testing: Advantages

Open source API testing tools offer numerous advantages that make them appealing for developers and organizations:

Cost-Effective: Open source tools are typically free to use, making them a budget-friendly choice for both small startups and large enterprises.

Community Support: These tools often have active and engaged communities of developers who contribute to their improvement and provide support through forums and documentation.

Flexibility: Open source tools can be customized to suit specific testing requirements, allowing testers to adapt them to their unique needs.

Transparency: Users have access to the source code, which enhances transparency and trust in the tool's functionality and security.

Wide Adoption: Many open source API testing tools have gained wide adoption in the industry, making them well-tested and reliable choices.

Popular Open Source API Testing Tools

Keploy: Keploy is an AI-powered testing tool that specializes in creating test cases and generating stubs/mocks for end-to-end testing. It can achieve an impressive 90% test coverage in just a matter of minutes using open-source testing resources. Keploy offers several notable features, including a straightforward Integration Framework for incorporating new libraries, the ability to convert API calls into test cases and data mocks, and the capability to handle a wide range of detailed test cases. Additionally, it supports four programming languages: Java, Node.js, Python, and Go. 

Postman: Postman is one of the most well-known API testing tools. It offers a user-friendly interface for creating and running API tests, as well as features for automating tests and generating documentation.

Swagger (OpenAPI): Swagger, now known as OpenAPI, provides a framework for designing, building, and documenting APIs. It includes tools for testing APIs based on their specifications.

REST Assured: REST Assured is a Java library for testing RESTful APIs. It simplifies API testing by providing a domain-specific language for writing tests.

SoapUI: While SoapUI is primarily known for testing SOAP-based web services, it also supports RESTful APIs. It offers both open source and commercial versions.

Karate: Karate is a unique open source API testing tool that combines API testing with behavior-driven development (BDD). It allows testers to write API tests in a natural language style.

Conclusion

Open source API testing tools have become essential assets for software development teams aiming to build reliable and robust APIs. Their cost-effectiveness, flexibility, and strong community support make them an attractive choice for organizations of all sizes. As APIs continue to play a pivotal role in modern software development, investing in open source API testing tools can help ensure the success of your projects while saving both time and money. Whether you are testing RESTful or SOAP-based APIs, there is likely an open source tool that suits your needs, enabling you to create and maintain APIs that perform flawlessly in today's interconnected digital world.

[bespokesoftwaredevelopment-blog]

If you need any help in the integration of HotesPro API, then feel free to contact us our expert developers are always ready to help you with the integration of any API or custom web development for you.

[robotico-digital]

Exploring the Power of Artificial Intelligence in API Testing Services

In the ever-evolving world of software development, APIs (Application Programming Interfaces) have become the backbone of modern applications. Whether it's a mobile app, web platform, or enterprise solution, APIs drive the data exchange and functionality that make these systems work seamlessly. With the rise of microservices and the constant need for faster releases, API Testing Services are more essential than ever.

However, traditional testing approaches are reaching their limits. As APIs become more complex and dynamic, manual or even scripted testing methods struggle to keep up. This is where Artificial Intelligence (AI) enters the picture, transforming how testing is performed and enabling smarter, more scalable solutions.

At Robotico Digital, we’ve embraced this revolution. Our AI-powered API Testing Services are designed to deliver precision, speed, and deep security insights—including advanced Security testing API capabilities that protect your digital assets from modern cyber threats.

What Makes API Testing So Crucial?

APIs enable communication between software systems, and any failure in that communication could lead to data loss, functionality errors, or worse—security breaches. That’s why API Testing Services are vital for:

Verifying data integrity

Ensuring business logic works as expected

Validating performance under load

Testing integration points

Enforcing robust security protocols via Security testing API

Without thorough testing, even a minor change in an API could break core functionalities across connected applications.

How AI Is Changing the Game in API Testing Services

Traditional test automation requires human testers to write and maintain scripts. These scripts often break when APIs change or evolve, leading to frequent rework. AI solves this by introducing:

1. Autonomous Test Creation

AI learns from API documentation, usage logs, and past bugs to auto-generate test cases that cover both common and edge-case scenarios. This dramatically reduces setup time and human effort.

2. Intelligent Test Execution

AI can prioritize tests that are most likely to uncover bugs based on historical defect patterns. This ensures faster feedback and optimized test cycles, which is especially crucial in CI/CD environments.

3. Adaptive Test Maintenance

API structures change frequently. AI automatically updates impacted test cases, eliminating the need for manual intervention and reducing test flakiness.

4. Continuous Learning & Improvement

AI algorithms improve over time by analyzing test outcomes and incorporating real-world performance insights.

5. AI-Driven Security Insights

With integrated Security testing API modules, AI can detect potential vulnerabilities such as:

Broken authentication

Sensitive data exposure

Injection attacks

Misconfigured headers or CORS policies

At Robotico Digital, our AI modules continuously monitor and adapt to new security threats, offering proactive protection for your API ecosystem.

Real-World Applications of AI in API Testing

Let’s break down how AI adds tangible value to API testing across different scenarios:

Regression Testing

When an API is updated, regression testing ensures that existing features still work as expected. AI identifies the most impacted areas, drastically reducing redundant test executions.

Load and Performance Testing

AI models simulate user traffic patterns more realistically, helping uncover performance issues under various load conditions.

Contract Testing

AI validates whether the API’s contract (expected input/output) is consistent across environments, even as the codebase evolves.

Security testing API

Instead of relying on static rules, AI-powered security tools detect dynamic threats using behavior analytics and anomaly detection—offering more robust Security testing API solutions.

Robotico Digital’s Approach to AI-Driven API Testing Services

Our commitment to innovation drives our unique approach to API Testing Services:

1. End-to-End AI Integration

We incorporate AI across the entire testing lifecycle—from test planning and generation to execution, maintenance, and reporting.

2. Custom AI Engines

Our proprietary testing suite, Robotico AI TestLab, is built to handle high-volume API transactions, real-time threat modeling, and continuous test adaptation.

3. Modular Architecture

We provide both on-premise and cloud-based solutions, ensuring seamless integration into your DevOps pipelines, Jira systems, and CI/CD tools like Jenkins, GitLab, and Azure DevOps.

4. Advanced Security Layer

Incorporating Security testing API at every phase, we conduct:

Token validation checks

Encryption standard verifications

Endpoint exposure audits

Dynamic vulnerability scanning using AI heuristics

This ensures that your APIs aren’t just functional—they’re secure, scalable, and resilient.

Elevating Security Testing API with AI

Traditional API security testing is often reactive. AI flips that model by being proactive and predictive. Here's how Robotico Digital’s Security testing API services powered by AI make a difference:

AI-Driven Vulnerability Scanning

We identify security gaps not just based on OWASP Top 10 but using real-time threat intelligence and behavioral analysis.

Threat Simulation and Penetration

Our systems use generative AI to simulate hacker strategies, testing your APIs against real-world scenarios.

Token & OAuth Testing

AI algorithms verify token expiration, scopes, misuse, and replay attack vectors—making authentication rock-solid.

 Real-Time Threat Alerts

Our clients receive real-time alerts through Slack, Teams, or email when abnormal API behavior is detected.

Tools and Technologies Used

At Robotico Digital, we utilize a blend of open-source and proprietary AI tools in our API Testing Services, including:

Postman AI Assist – for intelligent test recommendations

RestAssured + AI Models – for code-based test generation

TensorFlow + NLP APIs – for log analysis and test logic generation

OWASP ZAP + AI Extensions – for automated Security testing API

Robotico AI TestLab – our in-house platform with self-healing tests and predictive analytics

The Future of AI in API Testing Services

The integration of AI into testing is just beginning. In the near future, we can expect:

Self-healing test environments that fix their own broken scripts

Voice-enabled test management using AI assistants

Blockchain-verified testing records for audit trails

AI-powered documentation readers that instantly convert API specs into test scripts

Robotico Digital is actively investing in R&D to bring these innovations to life.

Why Robotico Digital?

With a sharp focus on AI and automation, Robotico Digital is your ideal partner for cutting-edge API Testing Services. Here’s what sets us apart:

10+ years in QA and API lifecycle management

Industry leaders in Security testing API

Custom AI-based frameworks tailored to your needs

Full integration with Agile and DevOps ecosystems

Exceptional support and transparent reporting

Conclusion

As software ecosystems become more interconnected, the complexity of APIs will only increase. Relying on traditional testing strategies is no longer sufficient. By combining the precision of automation with the intelligence of AI, API Testing Services become faster, smarter, and more secure.

At Robotico Digital, we empower businesses with future-proof API testing that not only ensures functionality and performance but also embeds intelligent Security testing API protocols to guard against ever-evolving threats.

Let us help you build trust into your technology—one API at a time.

[testrigtechnologies]

How AI and Machine Learning Are Transforming API Testing

APIs are the backbone of modern software, enabling seamless communication between applications, services, and platforms. However, as APIs grow in complexity, so do the challenges of testing them. Traditional API testing approaches often struggle to keep up with rapid development cycles, frequent updates, and dynamic environments. Enter AI and Machine Learning (ML)—game changers that are revolutionizing API testing by introducing intelligence, adaptability, and predictive capabilities into the process.

This article explores how AI and ML are reshaping API testing, providing deeper insights into automation, anomaly detection, self-healing tests, and the future of API quality assurance.

The Shortcomings of Traditional API Testing

Before diving into AI’s impact, it’s important to understand the limitations of traditional API testing methods:

Manual Test Case Writing – Time-consuming and error-prone.

Static Test Scripts – Break easily with frequent API updates.

Slow Defect Identification – Reactive rather than proactive.

Limited Scalability – Cannot efficiently handle large-scale testing needs.

These challenges highlight the need for AI-powered solutions that introduce intelligence, efficiency, and adaptability into API testing.

How AI and Machine Learning Are Revolutionizing API Testing

1. AI-Driven Test Case Generation – Smarter, Faster, and More Accurate

AI can analyze API documentation, past test data, and traffic patterns to auto-generate optimized test cases. This reduces manual effort and ensures comprehensive test coverage, even for complex API structures.

Example: Tools like Postman AI can generate test scripts by analyzing API request/response pairs, eliminating the need for manual scripting.

2. Self-Healing Test Automation – Say Goodbye to Script Maintenance

APIs are frequently updated, leading to broken test scripts. AI-driven self-healing automation detects these changes and dynamically updates the test scripts to maintain functionality.

How it Works:

AI identifies changes in API responses and structure.

It automatically updates impacted test cases.

The system learns from past test failures to improve resilience.

3. Predictive Analytics and Anomaly Detection – Finding Bugs Before They Occur

Machine learning algorithms analyze historical API performance data to detect anomalies, predict failures, and identify vulnerabilities before they impact users.

Key Benefits:

Detects outliers in API response times.

Identifies security threats like unexpected API payload manipulations.

Predicts performance degradation based on usage trends.

4. AI-Powered API Security Testing – Proactive Threat Detection

With the rise of API-based cyberattacks, traditional security testing is no longer sufficient. AI-driven security testing continuously monitors API traffic and detects suspicious activities in real-time.

Security AI can:

Identify authentication vulnerabilities (e.g., weak API keys, JWT token leaks).

Detect malicious API calls using behavior-based threat detection.

Simulate sophisticated cyberattacks (e.g., SQL injections, API fuzzing) to assess resilience.

5. Autonomous Load and Performance Testing – Real-World Traffic Simulation

AI enhances API performance testing by analyzing real-world usage patterns and dynamically adjusting test scenarios to reflect actual user behavior.

Example: AI-powered tools can analyze peak traffic patterns and predict potential bottlenecks before they occur, ensuring APIs can handle extreme loads effectively.

6. AI-Assisted API Documentation Testing – Ensuring Accuracy and Consistency

AI can compare API documentation against actual API responses to ensure consistency, detect outdated documentation, and even auto-generate missing documentation.

Tools like:

Swagger Inspector (for API spec validation)

Apicurio (for AI-assisted API design and documentation validation)

Conclusion

AI and Machine Learning are redefining API testing by making it brighter, faster, and more efficient. From automated test generation to self-healing automation and proactive security testing, AI-powered tools enable seamless, reliable, and future-ready API validation.

Enhance Your API Testing with Testrig Technologies

At Testrig Technologies, we specialize in AI-driven API testing services that ensure faster, more reliable, and cost-effective API validation.

Our expertise in intelligent automation and machine learning-based testing can help businesses achieve superior API quality.

Contact us today to transform your API Automation testing strategy with AI!

[dashofdelish]

API Testing: A Comprehensive Guide

In the fast-paced software development world, ensuring that different apps and services work together smoothly is super important. This is where API testing comes into the picture, and with tools like Postman, it's become much easier to do thorough testing. In this blog, we'll break down the basics of API testing, show you why automation tools matter, and how Techctice can guide you through this testing journey.

What is API Testing?

API testing, which stands for Application Programming Interface testing, is like checking that all the parts of a machine work correctly. It's about ensuring that different software parts communicate properly with each other. Imagine if you tapped on your phone's camera app and it actually opened – that's what API testing checks!

Using Postman for API Testing:

Imagine Postman as a helpful messenger between you and the software. It lets you send messages to the software and get responses back. It's like asking a question and getting an answer. With Postman, you can see if everything is working the way it should.

Why Automation Tools Matter:

Automation tools are like your personal helpers. They can do tests for you over and over again, like a robot doing a task repeatedly. This saves time and catches problems before they become big headaches.

Techctice knows how important it is to provide testers with the right tools and knowledge. On the Techctice platform, you'll find various resources to become a master in API testing using Postman and other tools. There are easy-to-follow tutorials, hands-on exercises, and practical situations to learn from. With Techctice, you can become skilled in using API testing with Postman, understanding what API testing is all about, exploring different API testing tools, and making the most of automation tools.

In short, API testing with Postman, understanding API testing, using API testing tools, and utilizing automation tools within Techctice are the key elements for a successful journey in API testing. Embrace these tools and concepts to ensure your software applications are reliable and top-notch. With Techctice guiding you, you'll be ready to tackle the changing world of API testing and make your mark in the field of QA.

[apisolutionsn2nsystems]

Unlock Seamless Transactions with Our Recharge and Bill Payment API Services

[mindfiresolutions-blog]

Blazor : Will it slay the JavaScript/SPA Frameworks?

It has been a long time since Microsoft allowed support of .NET directly on client-side. The last attempt was with Silverlight which got famous when it launched but due to lack of support from browsers and security concerns it stopped; like what happened to Adobe Flash.

……………………………………………………………………………………………………

What is BLAZOR?

Blazor is an attempt to bring .NET core to the browser directly which allows supported language like C# to run directly on the browser. Blazor allows developing Single Page Application (SPA) very quickly and by using a single language on both server and client.

Blazor allows having features similar to any other SPA framework like Routing, Template, Binding – one or two way, Rendering, etc.

The name comes by combining Browser and  MVC Razor i.e Blazor.

How does it work?

It entirely relies on WebAssembly (Wasm). Wasm was developed in 2015, to run high-level language directly on browsers. It is supported by W3C which means it is standard and could be utilized by different platform too. There are runtimes available to run C++ codes on browsers too. Since it is from W3C all latest browsers generally have the support of Wasm.

Blazor runs via Wasm but in-between there needs to be a runtime. Mono Wasm allows required .NET assemblies downloads on browser directly which runs on Mono Wasm Virtual Machine.

What are all advantages of Blazor?

A single roof of programming language to build client and server-side codes.

Wasm is generally faster than JavaScript (JS) because those are binary/compiled code. No need for parsing of scripts.

A typed system, a fewer scope of errors due to same. Razor, model binding, etc.

All sweet features of C# and .NET core: Dependency Injection (DI), delegates/events, etc.

Visual Studio Integrated Development Environment (IDE) to allow a rich experience to develop applications quickly with many provided inbuilt or plug-in supported features.

A fallback approach to enable Blazor to run if Wasm is not available to any browser. No issue of Garbage collection like all .NET platform, Mono Wasm supports it too.

Limitations of Blazor

Still is in the development phase, not recommended for production use.

Limited debugging support of Blazor codes since it runs on Wasm.

No way to access DOM elements has to rely on JavaScript for same.

The second biggest, there is not much components/control available in Blazor to have richer User Experience (UX). Like as simple as AutoSuggestion, we have to rely on JavaScript plugin for same.

Demo of Blazor

https://www.youtube.com/watch?v=IGj49kaYPEc The source code used in demo: https://github.com/viku85/BlazorDemo

Conclusion and Future

Blazor is easy to use with minimal effort in comparison to any JS SPA framework available in the market. Since it is on .NET, there is less learning curve for people coming from the .NET environment.

A lot of changes and development is going on with Blazor, many refinements, performance improvements, feature add-ons but still not having a final version to use on production. I believe with the support of controls like Kendo or inbuilt controls in Blazor, application development would be much quicker and feature rich.

We need to wait for the final release (if it happens) and a wide variety of supported controls which may kill JS Frameworks at least in the Microsoft development environment.

……………………………………………………………………………………………………

The views and opinions expressed in this article are those of the author. An avid techie, Vikash enjoys sharing his thoughts on computing and technology in his personal blog. To know more about our company, please click on Mindfire Solutions.