#Automation web application

[robotico-digital]

AI in Test Automation: Best Practices to Optimize Your QA Workflow

AI is revolutionizing test automation, allowing QA teams to move from reactive testing to proactive quality engineering. When implemented with a clear strategy, the right tools, and expert guidance, AI can drastically reduce testing time, improve accuracy, and empower continuous delivery. If you're looking to future-proof your QA process, now is the time to embrace AI-powered Automation Testing Services. With a trusted partner like Robotico Digital , you gain the tools, expertise, and support needed to transform your software testing and ensure long-term success.

[yethiconsulting]

The Future of Test Automation: Trends, AI, and the Role of QA Engineers

Introduction

Test automation is evolving rapidly, driven by emerging technologies like artificial intelligence (AI) and machine learning (ML). As software development accelerates, QA engineers must adapt to new tools, trends, and methodologies to ensure faster and more reliable testing.

Key Trends in Test Automation

AI-Powered Testing –

AI and ML are enhancing test automation by improving test script generation, self-healing test cases, and predictive analytics. Tools like Testim and Mabl use AI to optimize test execution.

Codeless Test Automation –

No-code/low-code platforms enable testers without programming skills to create automated tests, making web automation testing more accessible.

Shift-Left Testing –

Testing is integrated earlier in the development lifecycle, enabling faster bug detection and reducing rework costs.

Cloud-Based & Parallel Testing –

Cloud platforms like Sauce Labs and BrowserStack allow scalable, cross-browser testing with parallel execution, improving efficiency.

The Evolving Role of QA Engineers

With AI automating repetitive tasks, QA engineers are transitioning into test strategists and automation architects. They focus on designing robust automation frameworks, improving test coverage, and integrating AI-driven insights into testing strategies.

Conclusion

The future of test automation is AI-driven, cloud-based, and more strategic. QA engineers must upskill in AI, automation frameworks, and DevOps to stay ahead in this evolving landscape.

[jcmarchi]

Ericsson and AWS bet on AI to create self-healing networks

New Post has been published on https://thedigitalinsider.com/ericsson-and-aws-bet-on-ai-to-create-self-healing-networks/

Ericsson and AWS bet on AI to create self-healing networks

Ericsson’s Cognitive Network Solutions has joined forces with AWS to develop AI technologies for self-healing mobile networks.

Behind every text message and video call lies a complex system that telecom companies spend billions maintaining. This partnership between Ericsson and AWS aims to make those networks not just smarter, but virtually self-sufficient.

Jean-Christophe Laneri, VP and Head of Cognitive Network Solutions at Ericsson, said: “This collaboration marks a pivotal milestone in network optimisation technology.

“AWS’ global infrastructure and AI, alongside Ericsson’s unique cross-domain telecom experience and insights, will assist communication service providers in adapting to changing business conditions with predictable costs and enhanced operational efficiency.”

When the internet stops working at home, the first port of call for most is the “off and on again” approach: replug connections and restart the router. If that fails, call customer service. Using agentic AI, this partnership aims to automate the identification of problems, test solutions, and fix issues before you even notice. However, rather than just a home connection, the aim is to use agentic AI to do this on the massive scale of telecom networks serving potentially millions of people.

Fabio Cerone, General Manager of the EMEA Telco Business Unit at AWS, explained: “By working together, AWS and Ericsson will help telecommunications providers automate complex operations, reduce costs, and deliver better experiences for their customers. We are delivering solutions that create business value today while building toward autonomous networks.”

The technology works through something called RAN automation applications, or “rApps” in industry speak. These are sophisticated tools that can learn to manage different aspects of a network. The breakthrough comes from how these tools can now work together using agentic AI to improve networks, similar to colleagues collaborating on a project.

While the technology is undeniably complex, the potential benefits for everyday mobile users are straightforward. Networks that can anticipate problems and heal themselves could mean fewer dropped calls, more consistent data speeds, and better coverage in challenging areas.

For instance, imagine you’re at a football match with 50,000 other fans all trying to use their phones. Today’s networks often buckle under such pressure. However, a smarter and more autonomous network might recognise the gathering crowd early, automatically redirect resources, and maintain service quality without requiring engineers to intervene.

While traditional networks follow precise programmed instructions, the new approach tells the network what outcome is desired – like “ensure video streaming works well in this area” – and the AI figures out how to make that happen, adjusting to changing conditions in real-time.

While terms like “intent-based networks” and “autonomous management systems” might sound like science fiction, they represent a fundamental shift in how essential services are delivered. As 5G networks continue expanding and 6G looms on the horizon, the sheer complexity of managing these systems has outgrown traditional approaches.

Mobile operators are under tremendous pressure to improve service while reducing costs; seemingly contradictory goals. Autonomous networks offer a potential solution by allowing companies to do more with less human intervention.

As our dependence on reliable connectivity grows – supporting everything from remote healthcare to education and emerging technologies like autonomous vehicles – the stakes for network performance continue to rise. The partnership between these tech giants to create self-healing mobile networks signals recognition that AI isn’t just a buzzword but a necessary evolution for critical infrastructure.

See also: NVIDIA helps Germany lead Europe’s AI manufacturing race

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[atcuality3]

Next-Gen AR Development: Bringing Ideas to Life - Atcuality

Augmented reality is no longer just a futuristic concept—it’s here, transforming industries and enhancing real-world applications. At Atcuality, we specialize in custom AR solutions designed to create interactive, engaging, and scalable experiences for businesses of all sizes. Our augmented reality development services cater to diverse industries, including retail, tourism, real estate, and automotive, helping brands deliver unforgettable user experiences. Whether you need AR-powered product visualization, training simulations, or interactive storytelling, our expert team ensures a seamless, high-quality solution tailored to your needs. Elevate your business with Atcuality’s cutting-edge AR technology and shape the future of digital interaction today!

[vasundhara-infotech]

What is NLP? What is the Role of NLP in AI?

[itcompanyinindia]

[seozelenka]

Industrial Alarm Management Application

[todayiot]

IoT Platforms: The Backbone of Connectivity

IoT platforms are revolutionizing how businesses connect and manage smart devices. They simplify operations with device management, data analytics, and seamless integration while ensuring robust security.

Emerging trends like edge computing, AI integration, and 5G connectivity are pushing these platforms to new heights. At MeshTek, we create customized IoT solutions to help businesses thrive in this connected era.

Discover more at MeshTek’s IoT Services.

[atcuality1]

Custom AWS Solutions for Modern Enterprises - Atcuality

Amazon Web Services offer an unparalleled ecosystem of cloud computing tools that cater to businesses of all sizes. At ATCuality, we understand that no two companies are the same, which is why we provide custom Amazon Web Services solutions tailored to your specific goals. From designing scalable architectures to implementing cutting-edge machine learning capabilities, our AWS services ensure that your business stays ahead of the curve. The flexibility of Amazon Web Services allows for easy integration with your existing systems, paving the way for seamless growth and enhanced efficiency. Let us help you harness the power of AWS for your enterprise.

[nyuway]

The Role of DAST in Protecting Your Web Applications From Vulnerabilities

Nyuway

As cyber attacks become increasingly sophisticated, enterprises require DAST to help defend their applications against sophisticated cyber threats. By scanning a running application and simulating hacker behavior, it can identify vulnerabilities before enterprising hackers exploit them.

DAST complements static application security testing (SAST) and software composition analysis (SCA), offering additional runtime insights beyond source-code scans. Furthermore, it serves as a valuable companion to manual penetration testing.

Web Application

Dynamic application security testing (DAST) scans running web applications by simulating malicious external attacks and identifying vulnerabilities which could be exploited. DAST can reveal business logic flaws like SQL injection, XSS attacks and authentication issues which often go undetected through static code analysis tools (SAST) or manual penetration testing methods.

While DAST tools can be effective, they do have their limitations. False positives and lack of context can result in security gaps when applied solely. To address this limitation, it may be useful to combine DAST with other methodologies like SAST, IAST or software composition analysis (SCA) in order to create a comprehensive security program.

Implementing DAST into your CI/CD pipeline can ensure vulnerabilities are identified and fixed as code changes are made, leading to decreased costs and speedier time to production. Furthermore, early detection helps minimize accidental releases or potential data breaches; many of today's most harmful cyberthreats rely on unpatched vulnerabilities within running applications for attack.

API Security

DAST tools differ from SAST in that they attack an API without knowledge of its application; instead, this kind of testing mimics how attackers would try to exploit it - thus helping detect vulnerabilities which are harder to spot with traditional testing techniques.

DAST can be particularly effective at protecting web APIs. It can scan API endpoints to expose vulnerabilities that attackers could exploit, such as injection attacks or misconfigurations; and can identify unexpected data leaks or performance issues which might signal deeper security holes.

DAST excels at scanning web application UIs, yet struggles to access and test APIs tucked behind dynamic behavior layers. These layers hide backend API calls behind their respective UI layers until JavaScript code executes and uses an appropriate request format for runtime visibility.

Continuous

As web applications continue to be developed and evolve, security risks continue to shift and adapt accordingly. DAST can help address this challenge effectively.

This type of testing works by simulating attacks a malicious actor might employ to penetrate an application. By employing a black box approach and looking at it from outside in, this approach can detect vulnerabilities which other methods such as SAST or SCA fail to find.

DAST provides feedback and reporting to help developers and security teams prioritize vulnerabilities for remediation. It can also be easily integrated with the CI/CD pipeline to scan at every stage of development, making it easy to detect security issues before they reach production.

DAST can provide an overall picture of your application's vulnerability to threats when used alongside SAST & IAST (which examine code line by line), to form part of an integrated security assessment process. DAST tests entry points such as forms & API endpoints while SAST & IAST examine internal risks like misconfigurations & coding errors to provide a full assessment.

Automated Vulnerability Scanning

DAST differs from traditional static testing by testing an application while it runs, simulating how a hacker would search for vulnerabilities in real time. DAST can run both unauthenticated and authenticated modes to see how the app responds to attacks that typically gain control over an account and reveal sensitive data.

Businesses using advanced DAST solutions that utilize proof-based scanning can quickly identify and prioritize critical vulnerabilities using sophisticated DAST solutions that use proof-based scanning to eliminate false positives, making their teams focus their efforts on real risks that could cause serious breaches instead of spending hours sifting through massive test results.

DAST tools also give development and QA teams detailed information on how they can reproduce and fix vulnerabilities more quickly, so as to minimise disruption in production environments. When integrated into the Continuous Integration and Continuous Deployment pipeline, DAST can detect vulnerabilities at each stage of development and production to decrease chances of breach as well as ensure compliance with regulatory standards such as PSI-DSS or HIPAA.

Managed DAST Services

DAST is an essential part of any comprehensive application security program, and an indispensable element for its testing capabilities. As the most adaptable security testing tool on the market, it can be integrated into each stage of development from early design through quality assurance testing, staging deployment and production deployment. When integrated into an CI/CD pipeline DAST can also help developers identify vulnerabilities before reaching production, saving both time and money in development costs.

DAST works by conducting automated tests simulating external attack behavior without understanding its internals, similar to malicious attackers' tactics, in order to uncover unexpected outcomes and vulnerabilities. Language independent, DAST can detect runtime issues like server configuration problems, authentication/encryption misconfigurations and more that SAST cannot.

To maximize the value of DAST, organizations should set clear security objectives and incorporate it into existing CI/CD and DevOps workflows. This includes developing strategies for handling false positives and regression tests to verify previously fixed vulnerabilities do not resurface. Ideally, DAST should be integrated with CI/CD pipeline so every code push or deployment triggers dynamic security checks automatically.

[yethiconsulting]

Scaling Test Automation: Best Practices for Enterprise-Level QA

Scaling test automation for enterprise-level applications requires a strategic approach to ensure efficiency, reliability, and long-term success. As organizations expand their automation efforts, they must focus on maintainability, scalability, and collaboration.

One key best practice is building a robust automation framework that supports modular, reusable test scripts. A well-structured framework enhances test efficiency and minimizes redundancy. Additionally, implementing CI/CD integration ensures automated tests run seamlessly within the development pipeline, enabling faster releases with minimal manual intervention.

Enterprises should also prioritize parallel and cloud-based testing to speed up execution across multiple environments and devices. Cloud-based solutions reduce infrastructure costs and improve scalability.

Another critical factor is test data management. Large-scale automation requires a structured approach to generate, store, and retrieve test data dynamically, ensuring comprehensive test coverage.

AI-driven automation can further enhance scalability by enabling self-healing scripts, intelligent test case generation, and predictive analytics to optimize test execution.

Lastly, fostering cross-team collaboration between QA, developers, and DevOps teams ensures that automation aligns with business goals and delivers high-quality software efficiently. By following these best practices, enterprises can scale their test automation successfully while maintaining speed, accuracy, and cost-effectiveness.

[jcmarchi]

China's MCP adoption: AI assistants that actually do things

New Post has been published on https://thedigitalinsider.com/chinas-mcp-adoption-ai-assistants-that-actually-do-things/

China's MCP adoption: AI assistants that actually do things

China’s tech companies will drive adoption of the MCP (Model Context Protocol) standard that transforms AI assistants from simple chatbots into powerful digital helpers.

MCP works like a universal connector that lets AI assistants interact directly with favourite apps and services – enabling them to make payments, book appointments, check maps, and access information on different platforms on users’ behalves.

As reported by the South China Morning Post, companies like Ant Group, Alibaba Cloud, and Baidu are deploying MCP-based services and positioning AI agents as the next step, after chatbots and large language models. But will China’s MCP adoption truly transform the AI landscape, or is it simply another step in the technology’s evolution?

Why China’s MCP adoption matters for AI’s evolution

The Model Context Protocol was initially introduced by Anthropic in November 2024, at the time described as a standard that connects AI agents “to the systems where data lives, including content repositories, business tools and development environments.”

MCP serves as what Ant Group calls a “USB-C port for AI applications” – a universal connector allowing AI agents to integrate with multiple systems.

The standardisation is particularly significant for AI agents like Butterfly Effect’s Manus, which are designed to autonomously perform tasks by creating plans consisting of specific subtasks using available resources.

Unlike traditional chatbots that just respond to queries, AI agents can actively interact with different systems, collect feedback, and incorporate that feedback into new actions.

Chinese tech giants lead the MCP movement

China’s MCP adoption by tech leaders highlights the importance placed on AI agents as the next evolution in artificial intelligence:

Ant Group, Alibaba’s fintech affiliate, has unveiled its “MCP server for payment services,” that lets AI agents connect with Alipay’s payment platform. The integration allows users to “easily make payments, check payment statuses and initiate refunds using simple natural language commands,” according to Ant Group’s statement.

Additionally, Ant Group’s AI agent development platform, Tbox, now supports deployment of more than 30 MCP services currently on the market, including those for Alipay, Amap Maps, Google MCP, and Amazon Web Services’ knowledge base retrieval server.

Alibaba Cloud launched an MCP marketplace through its AI model hosting platform ModelScope, offering more than 1,000 services connecting to mapping tools, office collaboration platforms, online storage services, and various Google services.

Baidu, China’s leading search and AI company, has indicated that its support for MCP would foster “abundant use cases for [AI] applications and solutions.”

Beyond chatbots: Why AI agents represent the next frontier

China’s MCP adoption signals a shift in focus from large language models and chatbots to more capable AI agents. As Red Xiao Hong, founder and CEO of Butterfly Effect, described, an AI agent is “more like a human being” compared to how chatbots perform.

The agents not only respond to questions but “interact with the environment, collect feedback and use the feedback as a new prompt.” This distinction is held to be important by companies driving progress in AI.

While chatbots and LLMs can generate text and respond to queries, AI agents can take actions on multiple platforms and services. They represent an advance from the limited capabilities of conventional AI applications toward autonomous systems capable of completing more complex tasks with less human intervention.

The rapid embrace of MCP by Chinese tech companies suggests they view AI agents as a new avenue for innovation and commercial opportunity that go beyond what’s possible with existing chatbots and language models.

China’s MCP adoption could position its tech companies at the forefront of practical AI implementation. By creating standardised ways for AI agents to interact with services, Chinese companies are building ecosystems where AI could deliver more comprehensive experiences.

Challenges and considerations of China’s MCP adoption

Despite the developments in China’s MCP adoption, several factors may influence the standard’s longer-term impact:

International standards competition. While Chinese tech companies are racing to implement MCP, its global success depends on widespread adoption. Originally developed by Anthropic, the protocol faces potential competition from alternative standards that might emerge from other major AI players like OpenAI, Google, or Microsoft.

Regulatory environments. As AI agents gain more autonomy in performing tasks, especially those involving payments and sensitive user data, regulatory scrutiny will inevitably increase. China’s regulatory landscape for AI is still evolving, and how authorities respond to these advancements will significantly impact MCP’s trajectory.

Security and privacy. The integration of AI agents with multiple systems via MCP creates new potential vulnerabilities. Ensuring robust security measures across all connected platforms will be important for maintaining user trust.

Technical integration challenges. While the concept of universal connectivity is appealing, achieving integration across diverse systems with varying architectures, data structures, and security protocols presents significant technical challenges.

The outlook for China’s AI ecosystem

China’s MCP adoption represents a strategic bet on AI agents as the next evolution in artificial intelligence. If successful, it could accelerate the practical implementation of AI in everyday applications, potentially transforming how users interact with digital services.

As Red Xiao Hong noted, AI agents are designed to interact with their environment in ways that more closely resemble human behaviour than traditional AI applications. The capacity for interaction and adaptation could be what finally bridges the gap between narrow AI tools and the more generalised assistants that tech companies have long promised.

See also: Manus AI agent: breakthrough in China’s agentic AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[softweb-solutions]

Comprehensive Guide to Web Application Testing: Types, Use Cases & Tools

The success of your web application can be the linchpin of your business’s growth trajectory. Today’s modern business must not merely rely on functionality, but also on exceptional user experience, driving engagement and building customer loyalty.

According to IBM, the cost to fix bugs found during the testing phase could be 15x more than the cost of fixing those found during design.

As businesses increasingly rely on sophisticated web platforms to connect with their audiences, web application testing emerges as a strategic imperative. This blog delves into the transformative impact of web application testing services. Let’s explore different types of web application testing.

Understanding web application testing and why it is necessary

Web application testing is essential for ensuring its functions are accurate and meet the requirements of end users. To understand the testing process precisely, here is a comprehensive breakdown:

Purpose of web application testing

It aims to identify bugs, ensure functionality, and verify that the application meets the specific requirements. It ensures the application:

Functions correctly across different browsers and devices

Performs well under varying loads

Secure against vulnerabilities

Provides good user experience

Number of end users

It involves assessing how the application:

Handles high volume of users

Robustness under extreme conditions

Ability to scale efficiently with user demands

Target domain

Domain-specific testing: Based on target domains such as e-commerce, banking, healthcare, etc., it assures specific regulations and compliance requirements are met. Therefore, testing ensures that domain-specific functions such as payment processing and data security are working correctly and adhere to relevant standards.

Functional testing: It validates that all features and functions are relevant to the target domain and work as expected.

User role

Role-based testing: Various users have different login permissions and roles within an application, for example admin, regular user, and guests. Testing ensures that each role has appropriate access and functionalities, and unauthorized users do not have permission to access restricted areas.

User authentication and authorization testing: Verifies user authentication such as login mechanism and authorization such as role-based access control is working accurately.

Importance of AI in software testing AI in software testing improves software quality by analyzing data and identifying patterns. It optimizes the testing process, automates repetitive tasks, and analyzes vast datasets efficiently for more reliable software solutions. Read more

Further, web application testing is described in detail below:

Unit testing

It is a software testing method where the smallest part of an application such as units or components are tested in isolation from the rest of the system. These units are individual functions, methods, or classes validating each unit’s performance.

In 2023, unit testing used in 63% of all software projects was the most common type of software test. – Statista

For example:

Consider an online shopping cart

Add items to the cart:

Test adding a single item to the cart to verify it appears correctly.

Test adding multiple items to ensure the cart updates accurately.

Test adding items with various quantities to check the cart reflects the correct total.

Tools for unit testing:

JUnit: A widely used testing framework for Java applications.

NUnit: A robust testing framework for .NET applications.

Jest: A popular JavaScript testing framework.

PyTest: A flexible and scalable testing framework for Python.

Integration testing

It combines multiple units, modules, and components of software applications for testing and ensures communication and data flow functions are consistent as a cohesive unit. Its goal is to identify issues in the interaction between integrated components.

For example:

Consider an event registration system

User registration and event enrollment:

Verify user details are correctly passed from the registration module to the event enrollment system.

Ensure users can register, view their details, and successfully enroll in events.

Payment processing and confirmation:

Confirm the payment module securely processes transactions and confirms success.

Test that after payment, the registration status is updated, and a confirmation email or notification is sent.

Ensure errors during payment processing trigger appropriate messages and provide resolution instructions.

Tools for integration testing

JUnit: Used for testing in Java applications.

TestNG: A Java testing framework with advanced configuration capabilities.

Postman: A tool for API integration testing to validate HTTP requests.

SoapUI: A tool used for testing of web services, particularly for SOAP and REST APIs.

System testing

It is a comprehensive phase in the software testing lifecycle that evaluates the complete and integrated software system to ensure it meets specified requirements and functions as intended. This type of testing is conducted after integration testing and focuses on verifying the entire system’s behavior and performance in a unified environment.

For example:

Consider hotel reservation system

Verify that the user interface allows users to search for available rooms based on their selected criteria (e.g., dates, location, room type).

Test the booking form to ensure users can enter their details (name, contact information) and that the system displays accurate room availability and pricing information.

Check that users receive confirmation of their booking with a summary of their reservation details once they complete the process.

Tools for system testing

Selenium: A widely used tool for automating web application testing.

Cypress: An end-to-end testing framework designed for modern web applications.

Playwright: Open-source framework for end-to-end testing of web applications.

Performance testing

Evaluate how a system performs under various conditions, focusing on how application meets user expectations for speed, reliability, and stability, especially under varying operational conditions.

Assessing user experience under load: Evaluate how well the app maintains responsiveness and functionality when subjected to varying user loads and network speeds.

For example, test how the app performs when 5,000 users access it simultaneously under different network conditions.

Determining peak performance limits: Identify the maximum number of concurrent users the app can handle while keeping the response time within 2 seconds.

For example, ensure the app remains performant with 7,500 active users.

Evaluating performance on low-bandwidth connections: Test the app’s responsiveness and stability when accessed from a low-bandwidth network.

For example, 2G or slow 3G, to confirm that essential functionalities remain usable.

Tools for performance testing

Apache JMeter: An open-source tool to simulate heavy loads on web applications.

LoadRunner: Predicts system behavior under varying loads.

Gatling: Open-source tool designed to test web applications and services.

BlazeMeter: A cloud-based tool that supports JMeter scripts.

Security testing

It focuses on identifying vulnerabilities, weaknesses, and potential threats within an application or system. It aims to safeguard the applications from threats, ensuring compliance with security policies and protecting user data and system integrity, maintain user trust.

For example:

Authentication verification: Test the strength and effectiveness of user login mechanisms and credential storage.

Authorization checks: Ensure users have appropriate access rights and cannot access unauthorized areas or functions.

Vulnerability scanning: Identify and assess potential security vulnerabilities, such as SQL injection or cross-site scripting (XSS).

Penetration testing: Simulate attacks to identify weaknesses and test the system’s resilience against real-world threats.

Tools for security testing

OWASP ZAP (Zed Attack Proxy): Open-source tool for finding vulnerabilities in web applications.

Burp Suite: Comprehensive solution for web application security testing.

Acceptance testing

It determines whether a software application meets the acceptance criteria of the end-users or stakeholders and ensures the system fulfills business requirements and satisfies user needs.

For example:

Consider shopping experience on an e-commerce website

User registration and login:

Verify that new users can successfully register for an account by entering their personal information and receiving a confirmation email.

Test that returning users can log in with their credentials and access their accounts without issues.

Product search and purchase:

Ensure users can search for products using keywords or filters and view accurate search results.

Test that users can select products, add them to their shopping cart, and proceed to checkout.

Verify that users can enter shipping and payment information, apply discount codes, and complete the purchase.

Tools for acceptance testing

TestRail: Used for test case management.

Zephyr: A robust test management solution for end-to-end test planning and execution.

Suggested: Explore test automation with Healenium Automation testing

Automation testing

Automation testing involves using specialized tools to execute pre-scripted tests on the application. This type of testing is useful for repetitive tasks, regression testing, and load testing.

For example:

Efficiency: Speeds up the testing process by automating repetitive tasks.

Consistency: Ensures consistent execution of test cases.

Cost-effective: Reduces the manual effort involved, lowering costs in the long run.

Tools for automation testing

Selenium: A widely used tool for automating web application testing.

Cypress: An end-to-end testing framework designed for modern web applications.

Katalon Studio: Supports web, API, mobile, and desktop applications.

Appium: An open-source tool for automating mobile applications.

Robot Framework: A versatile open-source framework supports various testing libraries and tools.

Playwright: Open-source framework for end-to-end testing of web applications.

Regression testing

It ensures that recent changes or additions to an application have not adversely affected its existing functionality but involves re-running previously executed test cases verifying that new code changes, bug fixes, or enhancements have not introduced any new defects causing unintended side effects.

For example:

Feature enhancements: Ensure new features such as a recommendation engine on an e-commerce site, don’t disrupt existing functions such as the shopping cart.

Bug fixes: Verify fixing a bug, like in a banking app’s transaction history, doesn’t create new issues in other areas like transfers.

Performance optimizations: Confirm performance improvements such as faster image loading on a social media platform without affecting the existing features.

External system integration: Test integrating new systems or APIs such as email marketing tools in a CRM system, doesn’t disrupt current functionalities.

System upgrades: Check application features such as content publishing in CMS remain functional after system upgrades.

Tools for regression testing

Selenium (with CI/CD tools like Jenkins): Integrates with CI/CD pipelines to automate web applications.

TestComplete: Supports functional, regression, and load testing for desktop, web, and mobile applications.

Cypress: A JavaScript end-to-end testing framework to automate browser interactions.

Playwright: A Node.js library that automates web tests across Chromium, Firefox, and WebKit.

AI and ML are revolutionizing test automation AI and ML are revolutionizing test automation by resolving UI issues, enhancing functional and performance testing, and minimizing unrealistic data risk. This transformative impact makes testing more accurate and reliable. Read more

TestOps

TestOps is an emerging practice that integrates testing into the overall DevOps pipeline. It focuses on the management, orchestration, and analysis of testing activities across all phases of development. TestOps ensures that testing is aligned with continuous integration/continuous delivery (CI/CD) processes, enabling faster and more reliable releases.

Benefits of TestOps:

Enhanced collaboration: Bridges the gap between development, operations, and testing teams.

Continuous testing: Ensures that testing is a continuous activity integrated with the CI/CD pipeline.

Improved quality and speed: Accelerates the release process while maintaining high quality.

Tools for TestOps

Jenkins: CI/CD tool to automate the building, testing, and deployment of software.

CircleCI: A cloud-based CI/CD platform facilitates continuous integration and delivery workflows.

Azure DevOps: Provides a complete suite for end-to-end DevOps practices.

Gitlab: Integrated CI/CD functionality for managing the full DevOps lifecycle.

Smoke testing

It is a preliminary level of software testing conducted to ensure that the most critical functions of an application are working correctly before more detailed testing is performed.

For example:

1. Build verification

Before extensive testing begins, smoke testing verifies that the latest build is stable enough for further testing. For example:

E-commerce application: After deploying a new build, perform smoke testing to ensure that basic functions like login, product search, and checkout are working.

2. Feature release

When a new feature is added, smoke testing ensures that the new feature doesn’t break the existing core functionality. For example:

Social media app: After introducing a new messaging feature, conduct smoke testing to confirm that existing features like posting updates and notifications still work.

Tools for smoke testing

Selenium: Supports automated smoke testing for web applications.

JUnit: Widely used for Java applications.

TestNG: Framework for automated testing that can handle smoke tests.

Cypress: A JavaScript end-to-end testing to automate basic smoke test.

Playwright: A Node.js library that automates smoke tests across different browsers.

Load testing

Type of performance testing designed to assess how a system performs under a specific and an expected load of users or transactions. The primary objective is to determine whether the system can handle the anticipated volume of traffic or data without issues.

For example:

API performance: Test how an API handles 1,000 requests per minute to ensure it operates smoothly without errors or delays.

Database efficiency: Evaluate the database’s performance with multiple concurrent queries to ensure response times stay within acceptable limits.

Streaming services: Assess how a video streaming service manages 500 simultaneous HD streams to ensure quality and minimal buffering.

Tools for load testing

Apache JMeter: Widely used for performance and load testing of applications.

LoadRunner (Micro Focus): Enterprise-grade tool for load testing and performance monitoring.

Stress testing

It evaluates how a system behaves under extreme or peak conditions beyond its normal operating limits. It determines the system’s robustness, identify breaking points, and assess its ability to recover from failure.

For example:

System limits: Determine the maximum load the system can handle before failure, like handling 10,000 transactions per second.

Resource exhaustion: Assess how the system behaves when resources like CPU, memory, or storage are maxed out.

Error handling: Verify how the system manages and recovers from high-load scenarios that cause errors or crashes.

Tools for stress testing

Apache JMeter: Widely used for performance and load testing of applications.

LoadRunner (Micro Focus): Enterprise-grade tool for load testing and performance monitoring.

Insights Discover The Power of QA Automation Testing Services Assurance testing. Before the application is made public or sent to the client, it needs to have addressed every issue and bug that might have crept in during the coding of the application. Delegating a dedicated engineer to perform manual tests can get cumbersome. Download

Conclusion

As we navigate the intricate landscape of digital transformation, the role of comprehensive web application testing becomes undeniably pivotal to steering business growth. Beyond safeguarding functionality and performance, meticulous testing cultivates a resilient foundation for user satisfaction and operational excellence.

By embracing a strategic approach to testing, businesses can unveil latent opportunities, mitigate risks, and enhance their competitive edge. Talk to our QA test engineers to discuss your requirements.

Originally published at softwebsolutions on August 23, 2024.

[sm-techved]

Revolutionizing Application Testing in Agile Environments: Strategies for Success

In today's fast-paced digital landscape, traditional application testing methods often struggle to keep up with rapid development cycles. As businesses strive for quicker releases and continuous updates, the need for efficient and effective testing strategies has never been more critical. Enter the world of agile testing, where innovative approaches are transforming how we ensure software quality and drive innovation.

Agile testing is not merely about executing tests; it's about seamlessly integrating validation processes into the development workflow. By embracing agile methodologies, organizations can leverage cutting-edge techniques to enhance their software testing practices and deliver superior products to market faster than ever before.

key strategies for optimizing application testing in agile environments:

Embrace the "Shift Left" Approach

The "Shift Left" philosophy is fundamental to agile testing, emphasizing the importance of early application testing in the development process. Unlike traditional waterfall models, where testing occurs at the end, agile encourages validation from the outset. This approach incorporates unit testing and functional testing to ensure each component is thoroughly validated during development.

By integrating validation testing early, teams can significantly reduce defect inflow in the final product. This proactive strategy not only saves time but also minimizes costs associated with post-deployment bug fixes.

Harness the Power of AI Automation

As development speeds increase, manual testing often becomes a bottleneck. AI automation testing offers a powerful solution to this challenge. AI-powered tools can accelerate testing processes by identifying key areas for validation, generating test cases, and even predicting potential failure points.

AI automation is particularly effective in enhancing regression testing. By learning from past test results, AI can focus on areas with higher failure probabilities, improving test coverage and productivity. Moreover, AI-driven performance testing tools can simulate various user scenarios at scale, ensuring web application testing and mobile application testing are prepared for real-world usage.

Implement Continuous Testing with DevOps

In agile environments, the synergy between agile and DevOps practices is crucial. Continuous testing is the linchpin of this collaboration. By integrating testing into Continuous Integration/Continuous Deployment (CI/CD) pipelines, teams ensure that every code change undergoes automated testing before reaching production.

This approach accelerates the development process and enhances overall software quality. Automated tests, ranging from security testing to usability testing, are triggered with each commit, ensuring new features don't introduce vulnerabilities or compromise user experience.

Prioritize Agile Performance and Load Testing

As user expectations for application performance continue to rise, performance testing in agile setups becomes increasingly critical. Rather than conducting performance tests solely before release, agile advocates for continuous performance monitoring throughout the development cycle.

Modern tools enable teams to incorporate web application testing and mobile application testing into their agile workflows, providing real-time insights into application performance under various conditions. These continuous feedback loops help identify and address performance issues early, long before they impact end-users.

Emphasize User-Centric Testing

Agile methodologies place the user at the center of the development process. User testing involves frequent feedback loops that enable rapid iteration based on real user input. Similarly, accessibility testing should be ingrained in the process to ensure applications are usable by all, including those with disabilities.

Advanced AI-powered testing solutions can simulate a wide array of user interactions, providing valuable insights into how different user groups engage with applications. This information is invaluable for refining application design and functionality to meet genuine user needs.

Integrate Security Testing Throughout the SDLC

In an era of increasing cyber threats, security cannot be an afterthought. Agile development demands that security testing be integrated into every stage of the software development life cycle (SDLC). Automated security testing allows for continuous scanning of the codebase, identifying vulnerabilities as soon as they're introduced.

By embedding security testing within existing agile and DevOps processes, organizations can create more secure and resilient applications. This proactive approach ensures that software meets not only performance and usability standards but also rigorous security expectations.

Conclusion

Agile testing is revolutionizing how we approach software quality assurance, security, and user experience. By adopting these strategies, organizations can ensure their testing processes are as agile and responsive as their development cycles.

In a world where change is the only constant, staying ahead of the curve in testing methodologies is crucial. By embracing agile testing principles and leveraging cutting-edge technologies like AI automation, businesses can develop applications that are not only robust and secure but also deliver exceptional user experiences.

As we continue to navigate the complex landscape of software development, one thing is clear: agile testing is not just a trend—it's a necessity for organizations looking to thrive in the digital age. By investing in these advanced testing strategies, businesses can ensure their applications are ready to meet the challenges of tomorrow, today.

[seoperfectqa]

In today’s fast-paced digital landscape, ensuring the reliability and performance of web applications is crucial. With increasing user expectations and the need for rapid releases, manual testing alone cannot meet the demands. This is where automation testing for web applications becomes indispensable. In this comprehensive guide, we will explore best practices for automation testing, highlighting the importance, benefits, and effective strategies to implement automation in your testing process.

[todayiot]

What is the Use of BLE Mesh?

Bluetooth Low Energy (BLE) mesh is a game-changer for connected devices. It creates a robust, scalable network that allows multiple devices to communicate seamlessly over large areas. From smart homes to industrial IoT, BLE mesh enables secure, low-power communication between sensors, lights, and other devices, making it ideal for automation, remote control, and real-time monitoring. Whether you're controlling a city's streetlights or optimizing energy in a smart building, BLE mesh helps devices work together efficiently.