#codeless testing platform

[yethiconsulting]

UI Automation Testing: Reducing Manual Efforts and Boosting Accuracy

Introduction:

UI automation testing is a powerful method for reducing manual testing efforts while improving test accuracy and coverage. By automating repetitive tasks, QA teams can focus on more complex testing, leading to faster release cycles and higher software quality.

Automating Repetitive Tasks:

Manual UI testing can be time-consuming and prone to human error. Automating repetitive tasks—like form submissions, button clicks, and navigation—frees up testers to focus on more critical, exploratory testing. Tools like Selenium and Cypress allow you to automate these tasks, ensuring consistency across test runs.

Increasing Accuracy and Consistency:

Automation eliminates the variability introduced by human testers, ensuring the same steps are executed each time, thus improving the reliability of your tests. Automation also enables parallel testing across multiple browsers and devices, enhancing test coverage.

Faster Feedback with CI/CD Integration:

Integrating UI automation testing into CI/CD pipelines ensures that tests run automatically after each code change, providing faster feedback to developers. This helps catch bugs early in the development cycle, reducing the risk of costly errors.

Conclusion:

By automating UI testing, teams can significantly reduce manual efforts, increase accuracy, and accelerate development timelines. This leads to more reliable software and more efficient testing processes.

[qualityassurance11]

Best Practices for Successful Automation Testing Implementation 

Automation testing is an essential part of modern-day software development that accelerates delivery, reduces manual work, and improves software quality. But success in automation testing is not assured, it should be achieved by proper planning and execution along with proper compliance of best practices. 

In this blog, we will talk about key actionable strategies and best practices to ensure the successful implementation of automation testing in your projects. 

1. Start with a Clear Strategy 

Jumping straight into automation testing without a clear strategy will not always yield the desired results. Define the following: 

Objectives: Define the goals of the automation, whether it is about shorter test cycles, improved test coverage or eliminating human error. 

Scope: Set the areas of your application for automation and focus much on areas that have a high impact like regression and functional testing. 

Stakeholders: Get early involvement from the development, QA and product teams to avoid misalignment regarding expectations. 

A well-formed strategy helps guide the way and make sure everyone involved is aligned. 

2. Prioritize the Right Test Cases for Automation 

One of automation testing’s biggest mistakes with it is to use automation for everything. Rather than that, shape your test cases to that of: 

Are monotonous and time-consuming. 

Wherein critical for application functionality 

Have stable requirements. 

Some of these tests are regression tests, smoke tests, data-driven tests, etc. Do not automate the exploratory or highly dynamic tests that often get changed. 

3. Choose the Right Automation Tools 

The effectiveness of your automation testing initiative highly relies on appropriate tools selection. Look for tools that: 

Support the technology stack of your application (e.g., web, mobile, APIs). 

Give the flexibility to expand your project. 

Offer extensive reporting, reusability of scripts, and run across browsers. 

GhostQA is one example of a codeless platform that works well for teams across the skill set. GhostQA can let you focus on what matters and Auto Healing reduces your maintenance to enforce. 

4. Build a Strong Automation Framework 

An automation framework is the backbone of your automation testing process. It helps in standardization, reusability and scalability of test scripts. So, when you start designing your framework, make sure to leave some room for these features: 

Modularity: Split test scripts into reusable components 

Data-Driven Testing: Use Data-Driven Testing to separate test data from the scripts to provide flexibility. 

Error Handling: Install anti-malware solutions to prevent potential threats. 

A good framework streamlines collaboration and makes it easier to maintain your tests. 

5. Write High-Quality Test Scripts 

A good test script decides the reliability of your automation testing. To ensure script quality: 

When naming scripts, variables, or methods, use meaningful and descriptive names. 

For adaptability, you should leverage parameterization instead of hardcoding these values. 

Set up appropriate error-handling procedures for handling unforeseen problems. 

Do not add anything unnecessarily, the more complexity, the more difficult it is to debug and maintain. 

Tools such as GhostQA minimize the efforts put behind scripting providing no-code possibilities allowing even non-technical users to write robust tests. 

6. Regularly Maintain Your Automation Suite 

Even though automation testing is a great way to ensure quality in applications, one of its biggest challenges is keeping the test scripts updated with application changes. Keeping your test suite effective and up to date, regular maintenance. 

Best practices for maintenance include: 

Frequent Reviews: Conduct periodic audit of the test scripts to ensure that they are not outdated. 

Version Control: Utilize version control systems to maintain history of your script modifications. 

Auto-Healing Features: GhostQA and similar tools can track UI updates and modify scripts to reflect changes with little to no human intervention, minimizing maintenance costs. 

Take good care of your automation suite so that it doesn't become a liability. 

7. Address Flaky Tests 

Flaky tests—tests that pass or fail randomly—are a common issue in automation testing. They reduce trust in test results and take up time when debugging. To address flaky tests: 

Dig deeper into what might be the underlying causes — timing problems or dynamic elements. 

Use explicit waits instead of static waiting in tests to make them aligned with application behavior. 

Prefer smart detection-based tools (GhostQA, to be precise) to eliminate the chances of flaky tests. 

This translates into flourish as flakiness and is the most significant impact in strengthening confidence in your automation framework. 

8. Ensure Cross-Browser and Cross-Platform Compatibility 

Most modern applications work across many browsers and devices, so cross-compatibility testing is a necessity. Your automation testing suite must: 

Add test cases for popular browsers like Chrome, Firefox, Edge, and Safari. 

Testing across different operating systems on mobile (e.g., iOS/Android). 

GhostQA abstracts cross-browser and cross-platform testing so you can verify functionality in several types of environments without repeating yourself. 

9. Leverage AI and Smart Automation 

AI is revolutionizing automation testing with better efficiency and lesser maintenance cost. Next-generation tools like GhostQA powered by AI offer: 

Auto-Healing: Automatically adjust to any changes made to the app;such as modified UI elements 

Predictive Analysis: Showcase areas with the most potential high risk to prioritize tests. 

Optimized Execution: Run just the tests that yield the most performance insights. 

Use AI-Powered Tools as these can help you to increase the efficiency and accuracy of your testing. 

10. Monitor and Measure Performance 

To measure the effectiveness of your automation testing, you should track key metrics that include: 

Test Coverage: Number of automated tests covering application features. 

Execution Time: Time taken to execute automated test suites. 

Defect Detection Rate: Number of bugs detected in automation testing 

Flaky Test Rate: Frequency of inconsistent test results. 

Consistent assessment of these metrics helps in discovering the areas of improvement in your automation efforts while also exhibiting the ROI of the same. 

Conclusion 

So, the right approach of selecting the right tool and plan properly will help to do a successful automation testing implementation. This could be achieved by adopting best practices like prioritizing test cases, maintaining test scripts, making use of the AI-powered tools and collaborating with other stakeholders in the process. 

Tools like GhostQA, which come equipped with codeless testing, auto-healing features, and user-friendly interfaces, empower teams of both technical and non-technical backgrounds to streamline their automation processes and devote their attention to shipping quality software. 

[thara333]

Pioneering the Future of Software Quality Assurance through Automation Testing

Automation testing, a dynamic and essential software quality assurance technique, is reshaping the landscape of application testing. Its mission is to execute predefined test cases on applications, delivering heightened accuracy, reliability, and efficiency by automating tedious and time-consuming testing tasks.

The Present and Future Synergy in Automation Testing:

At its essence, automation testing responds to the pressing need for a streamlined testing process. Beyond its current significance, the future promises a paradigm shift, characterized by transformative trends and advancements.

Unveiling Future Trends in Automation Testing:

Proactive "Shift-Left" Testing: Embracing a proactive "Shift-Left" approach, the future of automation testing integrates testing earlier in the development life cycle. This strategic shift aims to detect and address issues at their inception, fostering a more resilient and efficient software development process.

Harmonizing with DevOps: Automation is positioned to become increasingly integral to DevOps practices. Its seamless integration into continuous integration and delivery (CI/CD) pipelines ensures not just faster but more reliable releases, aligning seamlessly with the agile principles of DevOps.

AI and Machine Learning Synergy: The convergence of artificial intelligence (AI) and machine learning (ML) is poised to revolutionize automation testing. This integration enhances script maintenance, facilitates intelligent test case generation, and empowers predictive analysis based on historical data, ushering in a new era of adaptive and efficient testing processes.

Evolving Cross-Browser and Cross-Platform Testing: In response to the diversification of the software landscape, automation tools are evolving to provide robust solutions for cross-browser and cross-platform testing. Ensuring compatibility across diverse environments becomes paramount for delivering a seamless user experience.

Codeless Automation Revolution: The ascent of codeless automation tools represents a pivotal shift in testing methodologies. This trend simplifies testing processes, enabling testers with limited programming skills to create and execute automated tests. This democratization of testing accelerates adoption across teams, fostering a collaborative testing environment.

Concluding the Journey: Navigating Future Imperatives:

In conclusion, automation testing transcends its current role as a necessity, emerging as a future imperative in the ever-evolving landscape of software development. As technologies advance and methodologies mature, automation testing is poised to play a pivotal role in ensuring the delivery of high-quality software at an accelerated pace. Embracing these future trends, the software industry is set to embark on a transformative journey towards more efficient, adaptive, and reliable testing processes.

[anilpal]

Exploring Genqe: The Future of Test Automation in the Cloud

Introduction to Genqe

In the rapidly evolving world of software development, ensuring high-quality applications at speed is a top priority. Genqe emerges as a cutting-edge solution, revolutionizing test automation in the cloud. This blog explores how Genqe is transforming the testing landscape, offering seamless, scalable, and efficient testing for modern applications.

What is Genqe?

Genqe is an advanced cloud-based test automation platform designed to streamline the software testing process. By leveraging the power of cloud computing, Genqe enables teams to perform automated testing across diverse environments, devices, and browsers without the burden of maintaining complex on-premise infrastructure. Its AI-driven capabilities and user-friendly interface make it a standout choice for developers and QA professionals aiming to accelerate release cycles while maintaining quality.

Key Features of Genqe

1. Cloud-Native Testing

Genqe harnesses the scalability of the cloud to provide access to thousands of real devices, browsers, and operating systems. This ensures comprehensive test coverage, allowing teams to validate their applications under real-world conditions without investing in physical device labs.

2. AI-Powered Automation

With built-in artificial intelligence, Genqe simplifies test creation and maintenance. Its self-healing test scripts adapt to changes in the application’s user interface, reducing manual intervention and minimizing test flakiness. This feature is particularly valuable for dynamic applications that undergo frequent updates.

3. Seamless CI/CD Integration

Genqe integrates effortlessly with CI/CD pipelines, enabling automated testing at every stage of development. This ensures rapid feedback loops, helping teams identify and resolve issues early, ultimately speeding up delivery without compromising quality.

4. Codeless Test Creation

For teams with varying technical expertise, Genqe offers a codeless automation approach. Testers can create and execute test cases using intuitive visual interfaces, making test automation accessible to non-technical users while maintaining robustness for complex scenarios.

5. Comprehensive Test Analytics

Genqe provides detailed analytics and reporting, offering insights into test execution, coverage, and performance. With visual dashboards, teams can quickly identify bottlenecks, track progress, and make data-driven decisions to optimize their testing strategy.

Benefits of Using Genqe

Scalability: Genqe’s cloud-based infrastructure allows teams to scale testing efforts effortlessly, supporting parallel test execution to reduce testing time.

Cost Efficiency: By eliminating the need for on-premise hardware, Genqe reduces infrastructure costs while providing access to a vast array of testing environments.

Faster Time-to-Market: Automated testing with Genqe accelerates release cycles, enabling teams to deliver high-quality software faster.

Enhanced Collaboration: Genqe’s cloud platform fosters collaboration by providing real-time access to test results, enabling distributed teams to work together seamlessly.

Why Genqe Stands Out

Unlike traditional testing tools, Genqe combines the power of AI with cloud scalability to address modern testing challenges. Its ability to handle dynamic applications, integrate with DevOps workflows, and support codeless automation sets it apart as a forward-thinking solution. Whether you’re testing web, mobile, or API-based applications, Genqe provides a unified platform to ensure quality across the board.

Getting Started with Genqe

To begin using Genqe, teams can sign up for a free trial on the Genqe platform. The onboarding process is straightforward, with step-by-step guides to set up test environments, create test cases, and integrate with existing tools. Genqe also offers robust customer support to assist with setup and optimization, ensuring a smooth testing experience.

Conclusion

Genqe is redefining test automation in the cloud, empowering teams to deliver high-quality software with unprecedented efficiency. Its AI-driven features, cloud scalability, and codeless approach make it an ideal choice for organizations aiming to stay ahead in the digital era. By adopting Genqe, businesses can streamline their testing processes, reduce costs, and accelerate innovation.

Ready to transform your testing strategy? Explore Genqe today and experience the future of test automation!

[tonymattny]

Power Up Software Testing with Codeless Test Automation

Introduction to Codeless Test Automation

Codeless test automation redefines software testing. This approach empowers teams to create, run, and maintain robust automated tests without writing code. ideyaLabs pioneers codeless test automation solutions in this rapidly evolving landscape.

Why Codeless Test Automation Changes the Game

Codeless test automation breaks the barrier to entry for non-technical testers. Teams build and execute tests using intuitive visual interfaces. Tester’s use drag-and-drop features, pre-designed modules, and keyword-driven actions.

Manual test creation consumes time and resources. Codeless platforms boost productivity. Testers focus on logic and strategy rather than syntax and scripting language.

Codeless test automation allows for faster adaptation to change. Test cases remain flexible and easy to update. ideyaLabs codeless solutions streamline every testing phase.

Key Features of Codeless Test Automation Platforms

Visual Test Design

Visual workflows replace traditional code. Users map out each test scenario step by step. This clarity reduces errors and miscommunication.

Reusable Components

Testers store and reuse common modules across different projects. Reusable assets optimize resources and standardize processes.

Seamless Integration

ideyaLabs platforms integrate with leading development and CI/CD tools. Teams establish end-to-end automation pipelines. Integration eliminates context-switching and boosts team efficiency.

Robust Reporting

Actionable analytics reveal clear insights. Dashboards show successes, failures, and bottlenecks. Teams diagnose issues fast and enhance quality quickly.

How ideyaLabs Drives Results with Codeless Test Automation

ideyaLabs leads clients through the automation journey. The team analyzes requirements, selects the ideal toolsets, and implements them with precision. ideyaLabs experts guide staff through onboarding and knowledge transfer. Codeless test automation becomes accessible for all skill levels.

ideyaLabs fosters a culture of continuous improvement. Regular feedback loops enhance product quality and team satisfaction. Customers accelerate release cycles and exceed quality goals.

Codeless Test Automation in Agile Environments

Fast-paced Agile development demands rapid feedback. Codeless test automation fits Agile seamlessly. Teams create and execute tests with each sprint. Automated tests verify every code update. ideyaLabs delivers solutions that adapt to Agile processes.

Small changes and frequent iterations never overwhelm testers. Visual test models scale quickly. Collaboration between testers, product owners, and developers enhances coverage and speeds up delivery.

Reducing Costs Through Codeless Test Automation

Codeless solutions minimize training needs. Organizations onboard new team members faster. The investment in skilled scripting decreases. ideyaLabs reduces reliance on specialized resources and external consultants.

Automated tests maintain themselves. Maintenance cycles require fewer hours. Costs drop as teams reuse test assets and handle updates through the visual interface rather than through manual scripting.

Scalability and Flexibility with ideyaLabs

Scalability stays at the core of ideyaLabs codeless automation. As projects grow, new functionalities join the automation suite smoothly. Teams stack new test modules without overhauling workflows.

Flexibility remains crucial for fast-moving teams. Codeless platforms handle different application types. Web, desktop, mobile, and API tests converge under one automation framework.

Best Practices for Codeless Test Automation

Start with Clear Test Objectives

Every automated test must link to a business goal. ideyaLabs consultants help define the metrics and outcomes for each phase of testing.

Maintain Consistent Test Design Structures

Standard naming conventions and logical flows make tests easier to understand and maintain. Documentation remains current and relevant.

Review and Optimize Regularly

Automated tests evolve with applications. Frequent reviews catch obsolete scripts. ideyaLabs guides optimization cycles to keep test suites lean and effective.

Promote Team Collaboration

Codeless test automation only succeeds with open communication. Cross-functional teams review, share, and refine test cases together.

Application of Codeless Test Automation Across Industries

Banking and Financial Services

Codeless test automation enforces strict compliance standards. Quick iterations meet regulatory needs. Automated test suites adapt to new security protocols.

Healthcare and Life Sciences

Healthcare solutions demand accuracy and confidentiality. Automated validation of functions ensures patient safety and regulatory compliance.

Retail and Ecommerce

Faster release cycles fuel customer engagement. Robust automation safeguards seasonal campaigns, inventory systems, and digital experiences.

ideyaLabs works with clients across every sector. Each industry receives a tailored approach to codeless test automation.

Addressing Common Myths About Codeless Test Automation

Some believe codeless platforms lack power or customization. ideyaLabs platforms offer advanced configurations, script injection options, and third-party integrations. Businesses achieve the depth needed for complex testing scenarios.

Others fear vendor lock-in. ideyaLabs offers export features and supports open standards. Teams retain full control over projects and data.

Evolving With Future-Proof Codeless Test Automation

AI and machine learning shape the future of test automation. ideyaLabs innovates by embedding smart algorithms in its platforms. Automatic maintenance and self-healing test scripts decrease downtime. Machine learning tracks application changes and reduces manual interventions.

Next-generation platforms deliver even more intelligent insights. Teams detect root causes of failures instantly and act decisively.

Conclusion: Drive Excellence With ideyaLabs Codeless Solutions

Codeless test automation accelerates software delivery. ideyaLabs empowers organizations to move beyond manual and code-heavy scripts. Visual automation, reusable components, seamless integrations, and smart analytics define the new era of quality assurance.

Quality, speed, and flexibility merge in the hands of every tester. ideyaLabs partners with you through each stage of adoption, helping achieve consistent success.

Embrace codeless test automation to define your competitive edge with ideyaLabs.

[globalindustrypulse]

Automation Testing Market Size Expected to Reach USD 63.05 Bn By 2032

The global automation testing market size was valued at USD 17.71 billion in 2024. It is projected to grow from USD 20.60 billion in 2025 to USD 63.05 billion by 2032, exhibiting a CAGR of 17.3% during the forecast period. With the accelerating shift to DevOps, agile development, and digital transformation, automation testing is becoming essential to reduce release cycles, improve test accuracy, and support CI/CD pipelines.

Key Market Highlights:

2024 Market Size: USD 17.71 billion

2025 Market Size: USD 20.60 billion

2032 Market Size: USD 63.05 billion

CAGR (2025–2032): 17.3%

Market Outlook: Strong growth trajectory fueled by AI-driven automation, cloud-based testing platforms, and the rising need for quality-at-speed

Leading Players in the Global Market:

IBM Corporation

Micro Focus (OpenText)

Capgemini

Tricentis

SmartBear Software

Parasoft

Keysight Technologies

Cigniti Technologies

Sauce Labs

Katalon

Tata Consultancy Services (TCS)

Accenture

Infosys

TestProject (by Tricentis)

BrowserStack

Request Free Sample PDF: https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/automation-testing-market-107180

Market Dynamics:

Growth Drivers:

Increasing Adoption of DevOps & Agile: Automation testing plays a pivotal role in continuous integration/continuous deployment (CI/CD) environments, enabling faster feedback and release cycles.

Demand for AI-Based Testing: Machine learning algorithms are being integrated to enhance test case generation, defect prediction, and self-healing capabilities.

Rising Complexity of Applications: With mobile-first, cloud-native, and multi-platform applications on the rise, automation is key to managing diverse testing needs.

Cost Efficiency and Time-to-Market: Automation significantly reduces regression testing costs, allowing enterprises to focus resources on innovation.

Cloud Testing Adoption: Scalable, on-demand cloud testing environments are helping enterprises run large-scale test scenarios efficiently.

Key Opportunities:

AI-Driven Test Automation Tools: Emerging platforms that use generative AI to create test scripts, simulate user behavior, and optimize test coverage

Codeless & Low-Code Automation: Rapidly growing market for tools that enable QA teams to write tests without deep programming skills

Test Automation for IoT and Embedded Systems: Expanding scope in automotive, healthcare, and industrial domains

Mobile App Automation: Increasing need for automated testing tools tailored to Android/iOS ecosystems and responsive design

Integration with RPA Platforms: Automation testing integrated with Robotic Process Automation (RPA) to streamline back-office workflows

Trends Shaping the Market:

Shift-Left and Shift-Right Testing Strategies: Earlier and later testing phases integrated with DevOps lifecycle

Digital Experience Testing: Increased focus on user journey simulation and UX performance in real-world conditions

AI-Based Self-Healing Tests: Use of ML to automatically detect changes in the UI and adjust test scripts accordingly

Test Data Management (TDM): Sophisticated tools being used to generate, mask, and manage test data across environments

Open Source Tool Adoption: Rising popularity of community-driven platforms for flexibility and cost reduction

Speak to Analyst: https://www.fortunebusinessinsights.com/enquiry/speak-to-analyst/automation-testing-market-107180

Technology & Application Scope:

Testing Types:

Functional Testing

Regression Testing

Performance Testing

API Testing

UI Testing

Security Testing

Deployment Modes:

On-Premise

Cloud-Based

Hybrid

End-Use Industries:

BFSI

Healthcare

Retail & E-commerce

Manufacturing

IT & Telecom

Government

Media & Entertainment

Tools & Frameworks:

Selenium

Appium

TestComplete

JUnit

Cypress

Ranorex

Robot Framework

Playwright

Recent Developments:

April 2024 – Tricentis launched Tricentis Testim, an AI-powered test automation platform for browser-based and mobile apps with self-healing capabilities.

January 2024 – Microsoft added enhanced automated testing features to Azure DevOps, enabling developers to embed AI-assisted test execution and visual bug tracking.

October 2023 – BrowserStack acquired Nightwatch.js, boosting its automation testing toolkit for full-stack JavaScript developers.

Conclusion:

The global automation testing market is experiencing rapid growth as businesses prioritize speed, quality, and innovation in their software development lifecycles. With emerging AI tools, robust cloud infrastructure, and the growing complexity of digital ecosystems, the future of automation testing is intelligent, adaptive, and deeply integrated into business workflows.

[businessindustry]

Automated Testing Software Market Global Industry Analysis | Trends 2025-2033

The Reports and Insights, a leading market research company, has recently releases report titled “Automated Testing Software Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2025-2033.” The study provides a detailed analysis of the industry, including the global Automated Testing Software Market share, size, trends, and growth forecasts. The report also includes competitor and regional analysis and highlights the latest advancements in the market.

Report Highlights:

How big is the Automated Testing Software Market?

The global automated testing software market was valued at US$ 6.6 billion in 2024 and is expected to register a CAGR of 7.4% over the forecast period and reach US$ 12.6 billion in 2033.

What are Automated Testing Software?                                 ��                                                                                                           

Automated testing software comprises tools and frameworks created to automate the running of tests for software applications. It enables developers and quality assurance teams to efficiently test software functionality, performance, and security by executing pre-scripted tests. This software can mimic user interactions, detect bugs and errors, and provide comprehensive reports on test outcomes. Its use enhances testing speed and accuracy, resulting in quicker development cycles and improved software quality.

Request for a sample copy with detail analysis: https://www.reportsandinsights.com/sample-request/1735

What are the growth prospects and trends in the Automated Testing Software industry?

The automated testing software market growth is driven by various factors and trends. The market for automated testing software is experiencing rapid expansion within the software industry, propelled by the widespread adoption of agile and DevOps methodologies. This market encompasses a range of tools and platforms aimed at automating testing processes, enhancing software quality, and expediting time-to-market. Key drivers of market growth include the increasing requirement for continuous testing, the imperative to cut down testing expenses and time, and the escalating complexity of software applications. Additionally, the market is influenced by the escalating trend of digital transformation across industries, which necessitates dependable testing solutions to ensure software applications' reliability and performance. Hence, all these factors contribute to automated testing software market growth.

What is included in market segmentation?

The report has segmented the market into the following categories:

By Type of Testing:

Functional Testing

Performance Testing

Security Testing

Compatibility Testing

Usability Testing

Others

By Testing Tool:

Test Management Tools

Functional Testing Tools

Performance Testing Tools

Security Testing Tools

Codeless Testing Tools

Others

By Deployment Mode:

On-Premises

Cloud-based

By Organization Size:

Small and Medium-sized Enterprises (SMEs

Large Enterprises

By End-User Industry:

IT and Telecom

BFSI (Banking, Financial Services, and Insurance)

Healthcare

Retail

Manufacturing

Government and Public Sector

Media and Entertainment

Others

Segmentation By Region:

North America:

United States

Canada

Europe:

Germany

The U.K.

France

Spain

Italy

Russia

Poland

BENELUX

NORDIC

Rest of Europe

Asia Pacific:

China

Japan

India

South Korea

ASEAN

Australia & New Zealand

Rest of Asia Pacific

Latin America:

Brazil

Mexico

Argentina

Middle East & Africa:

Saudi Arabia

South Africa

United Arab Emirates

Israel

Who are the key players operating in the industry?

The report covers the major market players including:

IBM Corporation

SmartBear Software

Tricentis

Ranorex GmbH

TestComplete

Selenium

Appium

Sauce Labs Inc.

Parasoft Corporation

Eggplant (formerly TestPlant)

Cigniti Technologies

QualiTest Group

Broadcom Inc. (formerly CA Technologies)

Hewlett Packard Enterprise (HPE)

View Full Report: https://www.reportsandinsights.com/report/Automated Testing Software-market

If you require any specific information that is not covered currently within the scope of the report, we will provide the same as a part of the customization.

About Us:

Reports and Insights consistently mееt international benchmarks in the market research industry and maintain a kееn focus on providing only the highest quality of reports and analysis outlooks across markets, industries, domains, sectors, and verticals. We have bееn catering to varying market nееds and do not compromise on quality and research efforts in our objective to deliver only the very best to our clients globally.

Our offerings include comprehensive market intelligence in the form of research reports, production cost reports, feasibility studies, and consulting services. Our team, which includes experienced researchers and analysts from various industries, is dedicated to providing high-quality data and insights to our clientele, ranging from small and medium businesses to Fortune 1000 corporations.

Contact Us:

Reports and Insights Business Research Pvt. Ltd. 1820 Avenue M, Brooklyn, NY, 11230, United States Contact No: +1-(347)-748-1518 Email: sales@reportsandinsights.com Website: https://www.reportsandinsights.com/ Follow us on LinkedIn: https://www.linkedin.com/company/report-and-insights/ Follow us on twitter: https://twitter.com/ReportsandInsi1

[fantastictheoristdonut]

IPv6 proxy access is limited? Why mainstream platforms do not support IPv6

Ⅰ. Why are IPv6 proxies frequently restricted? Analysis of key reasons

Although IPv6, as a new generation protocol, has massive address resources, its popularization still faces multiple obstacles. As of 2025, only about 45% of network services in the world fully support IPv6. The main reasons include:

1. High cost of infrastructure upgrade: It takes millions of dollars to transform the network architecture, and some cloud services, payment gateways, etc. are not adapted to IPv6.

2. Security policy restrictions: IPv6 addresses are easily used for DDoS attacks, and platform firewalls often filter unconventional protocol traffic by default.

3. Differences between regions and operators: The IPv6 penetration rate in Europe and the United States exceeds 60%, while it is only 35% in the Asia-Pacific region. Cross-border business is easily blocked due to regional policies or operator configuration conflicts.

Ⅱ. List of common platforms that do not support IPv6 (actual test in 2025)

According to the latest test of the BitBrowser Team, the following platforms still have IPv6 access restrictions:

1. Social media: TikTok (web), Twitter API, Pinterest media interface

2. E-commerce platform: Etsy payment gateway, Japan Rakuten logistics system, Carousell image server.

3. Financial payment: PayPal risk control system marks IPv6 as high risk, and TransferWise cross-border interface is only compatible with IPv4.

4. Streaming media: Hulu copyright detection node, DAZN live broadcast distribution server.

Note: Although some platforms such as Facebook and YouTube support IPv6, there may be compatibility differences between regional servers. It is recommended to use DNS Checker to detect AAAA records in real time.

III. Efficient solution: compatibility first + tool empowerment

1. Proxy type selection strategy

• Prioritize the use of IPv4 proxy: covering more than 90% of platforms to avoid triggering risk control. For example, BitBrowser recommends that IP service providers provide pure residential IP, supporting e-commerce, social media and other scenarios.

• Alternative NAT conversion technology: access restricted platforms through IPv6 to IPv4 gateway, but be aware that it may increase latency by more than 50ms.

2. Tool recommendation: BitBrowser

For multi-account operation scenarios, BitBrowser can significantly improve access security and efficiency:

• Multi-account isolation management: assign independent browser fingerprints (such as time zone, font, Canvas parameters) to each account, simulate the real device environment, and reduce the risk of association.

• Intelligent proxy configuration: support HTTP/Socks5 protocol, one-click binding IPv4 proxy IP, and achieve "one account, one IP". Actual measurement shows that with the BitBrowser recommended IP service provider, 80% of verification code pop-up windows can be reduced.

• RPA automated operation: built-in codeless script function, automatically complete repetitive tasks such as TikTok likes and e-commerce product listings, and improve operational efficiency.

• Dual-core engine: based on two browser kernels of Chromium and Firefox, flexibly adapt to anti-crawling strategies of different platforms.

IV. Operation Guide: 3 steps to achieve secure access

1. Detect IP type:

Confirm that the current proxy is in IPv4 format (such as 192.168.x.x) through WhatIsMyIP or ipleak.net.

2. Configure the Bitbrowser environment:

• Create an independent browser configuration file and fill in the IPv4 proxy information (host + port + account password).

• Enable the "Fingerprint Randomization" function to simultaneously modify 20+ parameters such as User-Agent and time zone.

3. Batch task execution:

V. Summary and Outlook

Although IPv6 is the future trend, cross-border business still needs to focus on compatibility at this stage. The "IPv4 proxy + fingerprint browser" combination solution can effectively break through access restrictions and ensure account security. It is recommended to pay attention to platform policy changes regularly and use tools such as BitBrowser to achieve long-term and stable operations.

Experience now: Go to the BitBrowser official website to download the client, register and get 10 free browser environments, and start an efficient cross-border journey.

[cybersecurityict]

Automation Testing Market Size, Share, Analysis, Forecast, and Growth Trends to 2032: Software Quality Becomes a Strategic Priority

The Automation Testing Market was valued at USD 29.4 billion in 2023 and is expected to reach USD 118.6 billion by 2032, growing at a CAGR of 16.80% over 2024-2032.

The Automation Testing Market is experiencing a transformative shift as businesses increasingly prioritize faster time-to-market and enhanced software quality. Driven by the rising demand for digital transformation and agile methodologies, automation testing has become a critical element in software development lifecycles. Organizations are embracing automated solutions to minimize manual intervention, reduce errors, and ensure consistent performance across platforms.

Automation Testing Market continues to gain traction across sectors such as BFSI, healthcare, retail, telecom, and manufacturing. With the growing complexity of applications and integration of AI and machine learning into testing tools, the market is evolving rapidly. Enterprises are not just automating repetitive test cases but also leveraging intelligent automation to predict defects, improve test coverage, and enhance user experiences.

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Market Keyplayers:

Selenium - Selenium WebDriver

Tricentis - Tosca

IBM - Rational Functional Tester

Micro Focus - UFT (Unified Functional Testing)

SmartBear - TestComplete

Cucumber - CucumberStudio

Appium - Appium

Katalon - Katalon Studio

Ranorex - Ranorex Studio

Worksoft - Worksoft Certify

Sahi - Sahi Pro

LambdaTest - LambdaTest

Eggplant - Eggplant Functional

Perfecto - Perfecto Test Automation

TestComplete - TestComplete

Telerik - Test Studio

Sauce Labs - Sauce Labs Selenium Grid

Applitools - Visual AI Testing

Postman - Postman API Testing

Tricentis – qTest

Market Analysis

The automation testing landscape is driven by rapid technological advancements and the increasing need for efficient, error-free deployment processes. Businesses are adopting DevOps and continuous integration/continuous deployment (CI/CD) pipelines, fueling the demand for scalable and flexible testing solutions. Cloud-based testing platforms and open-source tools have further expanded accessibility, enabling SMEs to compete alongside large enterprises. The growth of mobile and web applications has also intensified the requirement for robust cross-platform testing frameworks.

Market Trends

Rise of AI and Machine Learning: Intelligent automation is empowering predictive analytics in testing, enhancing accuracy and speed.

CI/CD Integration: Seamless integration of automation tools into CI/CD pipelines accelerates development cycles.

Cloud-Based Testing: Scalable cloud infrastructure enables remote and parallel testing environments.

Codeless Automation Tools: These tools are lowering the technical barriers, making automation accessible to non-developers.

Increased Adoption in SMEs: Open-source frameworks and SaaS models are driving adoption across small and medium businesses.

Market Scope

Expanding Across Verticals: Automation testing is being adopted in healthcare, eCommerce, BFSI, telecom, and automotive industries.

Global Penetration: North America leads, but Asia-Pacific is emerging rapidly with digitization initiatives.

Beyond Functional Testing: Growth in performance, security, and usability testing automation.

Support for Emerging Tech: Evolving tools support IoT, blockchain, and AR/VR applications.

High ROI Potential: Reduced human error, faster testing cycles, and better product quality.

As the demand for seamless user experiences and reliable digital platforms grows, the market's potential continues to expand. From startups to multinational corporations, the focus on automation is no longer optional but essential.

Market Forecast

The future of the Automation Testing Market is poised for exponential growth, powered by innovation and demand for faster, higher-quality releases. As enterprises transition from legacy testing methods to modern, AI-driven frameworks, automation is expected to redefine how software is built, tested, and delivered. With continuous investments in R&D and vendor competition heating up, the market is set to witness breakthroughs in intelligent test orchestration, self-healing scripts, and hyper-automation capabilities.

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Conclusion

In a world where digital agility defines success, the Automation Testing Market stands as a pivotal force in accelerating innovation. For businesses aiming to stay competitive, embracing automation isn't just a trend—it's a strategic imperative. Whether you're a tech leader, a QA professional, or an enterprise innovator, now is the time to capitalize on the wave of transformation driving this dynamic market forward.

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SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

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[testrigtechnologies]

What is Codeless Automation and How its work?

As software development cycles grow faster and more continuous, testing needs to move at the same velocity. Traditional automation—powerful though it is—can become a bottleneck when only a small group of engineers can write and maintain test scripts. Enter codeless test automation, a modern answer to the challenge of scaling quality across teams without requiring everyone to write code.

But codeless is more than just a buzzword—done right, it’s a collaborative, intelligent, and scalable testing methodology that’s redefining how organizations approach QA.

What Is Codeless Test Automation?

Codeless test automation refers to the use of platforms and tools that allow testers to create, execute, and maintain automated tests without writing traditional programming code. Instead of scripting in languages like Java or Python, testers interact with:

Drag-and-drop interfaces

Pre-built test blocks or visual workflows

Natural language inputs or behavior-driven design formats (like Gherkin)

These tools abstract the code behind the scenes, allowing both technical and non-technical team members to contribute to the automation process.

Low-Code vs. No-Code vs. Codeless Automation: Understanding the Differences

Although often used interchangeably, these terms are not the same:

Low-Code Automation provides a blend—it offers visual interfaces but also allows code injections for complex conditions. Perfect for semi-technical testers who want both control and ease.

No-Code Automation eliminates code entirely. It's built for business users and testers with no programming background. Simplicity is the goal—but often at the cost of flexibility.

Codeless Automation, as a broader term, may incorporate both low-code and no-code options. It focuses on abstracting complexity while still offering enough control for power users when needed.

Read also: Best Automation Testing Tools

How Does Codeless Testing Work?

Let’s walk through how a modern codeless automation platform functions:

1. Test Creation

You begin by interacting with the application under test (AUT)—clicking, typing, or performing other actions. The tool records these actions and translates them into a structured test case. Some platforms also allow building tests visually—connecting steps like flowchart blocks or writing plain English test scenarios.

2. Object Recognition

Modern tools use AI-powered selectors or smart locators that adapt when UI elements change. This is crucial because flaky tests are often caused by fragile selectors.

3. Test Data Integration

Need to run the same test for different user types or datasets? Codeless tools can link to spreadsheets, databases, or data generators—without scripting loops or variables.

4. Execution & Scheduling

Tests can be executed locally, on the cloud, or across real devices and browsers. You can schedule them daily or hook them into CI/CD tools like Jenkins, GitHub Actions, or Azure DevOps.

5. Reporting & Analysis

Post-execution, you get visual dashboards, logs, screenshots, and detailed analytics. Some tools even auto-file bugs in Jira when a test fails.

Which Tests Are Best Suited for Codeless Automation?

Not every test type fits codeless automation. It shines in areas like:

• UI Regression Tests

When your product UI evolves frequently, regression test coverage can grow exponentially. Codeless tools make it easier to keep up without burning out your dev team.

• Smoke Tests

Want to validate login, dashboard loading, or payment gateway availability with every build? Codeless tools help you get quick feedback without writing dozens of scripts.

• End-to-End User Journeys

For tests that simulate real-world user paths—like signing up, purchasing a product, and logging out—codeless testing maps these flows efficiently and understandably.

• Cross-Browser / Device Testing

Codeless platforms often integrate with device farms (like BrowserStack or Sauce Labs), letting you run the same test across multiple environments without duplication.

When Not to Use Codeless Automation

Despite its power, codeless isn’t a silver bullet.

Highly complex workflows involving encrypted data, chained APIs, or backend validations still need traditional scripting.

Performance testing, load testing, and deep service-layer tests are beyond the reach of most codeless tools.

If your team needs 100% control over logic, libraries, and exceptions, coded automation is still king.

Final Thoughts

Codeless automation is about making test automation accessible, collaborative, and scalable. It’s not about replacing developers—it's about enabling QA teams to move faster and contribute earlier.

When adopted strategically, codeless testing can reduce time-to-market, increase test coverage, and empower entire teams to contribute to quality.

Want to Get Started With Codeless Automation?

At Testrig Technologies, As a leading Automation Testing Company, we specialize in integrating codeless tools into robust testing ecosystems—balancing ease with enterprise-grade power.

📩 Reach out for a free strategy session, and let’s build a smarter, faster, more inclusive QA process—together.

[yethiconsulting]

Best Practices for Test Management in a Continuous Integration/Delivery (CI/CD) Environment

Introduction

Start by explaining the role of Continuous Integration (CI) and Continuous Delivery (CD) in modern software development. Emphasize that effective test management is crucial to maintaining quality in fast-paced, automated pipelines.

Early and Continuous Testing

Implement testing at every stage of the CI/CD pipeline, starting from the earliest phases. Unit, integration, and functional tests should be run continuously to catch defects early.

Automation Integration

Test automation is vital for CI/CD environments. Ensure that all repetitive tests, such as regression tests, are automated. Use tools like Selenium, Jenkins, and Travis CI to integrate automated tests into the pipeline.

Effective Test Case Management

Organize test cases in a way that they can be easily maintained and executed in CI/CD pipelines. Regularly review and update test cases to ensure they are relevant and up-to-date.

Use of Test Management Tools

Leverage test management tools like TestRail or Zephyr that integrate with CI/CD platforms. This ensures seamless tracking, reporting, and scheduling of tests within the automated process.

Monitoring and Reporting

Set up real-time monitoring and reporting for continuous feedback on test results. This helps in quickly identifying issues and improving collaboration between developers and QA teams.

Conclusion

Summarize by stating that efficient test management in a CI/CD environment enhances the speed and reliability of software delivery while maintaining high-quality standards.

[govindhtech]

Terra Quantum Debuts Qode Engine And QAI Hub for QML

TQ42 Studio's Closed Beta: QAI Hub Makes Quantum AI Commonplace

On World Quantum Day, Rethinking Accessibility and Breaking Down Barriers with a No-Code Quantum ML Milestone

Terra Quantum launches TQ42 Studio Closed Beta on World Quantum Day. QAI Hub and Qode Engine make up this ecosystem, which aims to accelerate quantum AI deployment. QAI Hub, a no-code quantum machine learning platform, is the launch's centrepiece, even if Qode Engine (Python SDK) allows advanced developers.

Importance of Quantum AI

Quantum AI might revolutionise supply chain optimisation and industrial forecasting, but most teams cannot afford it. Specialised expertise and high coding requirements delay adoption. Terra Quantum and TQ42 Studio want to foster quantum AI innovation:

Increase Model Generalisation

Quantum neural networks may catch subtle data patterns, making models more responsive to real-world complexity.

Encode Richer Exponential Data

Quantum states' exponential data encoding allows higher-dimensional exploration without exponential processing expenses.

Smaller Datasets Deepen Understanding

Hybrid quantum-classical layers can quickly uncover patterns with less data, which is important for R&D.

Make More Reliable Predictions

Quantum machine learning (ML) can increase prediction accuracy from near-real-time optimisation to sophisticated forecasting.

Terra Quantum created the QAI Hub in TQ42 Studio to make experiment-driven R&D safer, upskill teams faster with (changing) TQ Academy courses, and boost accessibility.

Beta version of QAI Hub: Quantum Machine Learning Without Code

QAI Hub, Terra Quantum's no-code platform, simplifies quantum-enhanced AI prototyping. Teams who wish to analyse ideas and build quantum skills without complex programming will love it. QAI Hub cherishes your comments as it develops to make quantum AI open to anyone in beta.

Beta test the QAI Hub.

Accessible quantum ML

Creating hybrid quantum-classical models is easy without quantum expertise. employ QAI Hub's TQml tools to employ our powerful Hybrid Quantum Neural Networks.

Codeless Model Builder

Use of quantum neural networks has never been so easy! You can build and train a QML model in 5 visual steps without coding, starting with automated data processing.

Flexibility of Hybrid Computing

Try quantum machine learning with HPC (CPUs, GPUs) and QPU access. This method is good for concept validation and prototyping.

Agentic AI Help

As your conversational AI helper, TQ Copilot tackles laborious model design and tuning. Let TQai manage quantum neural networks while you focus on your goals.

Integration with TQ Academy

Early access to Terra Quantum's professionals' learning resources; more in-depth courses to follow.

How does QAI Hub work?

Five easy steps to construct, adjust, and run quantum machine learning models:

Import and prepare data safely with automated tools.

Create quantum-classical models with the visual interface with TQ Copilot.

Your models will perform seamlessly on CPUs, GPUs, and QPUs.

Evaluation & Optimisation: Improve models with automated hyperparameter tweaking and advanced evaluation tools.

Prediction and Scaling: Make accurate projections and scale your successful trials to corporate deployments.

The Promise of Quantum AI

Increase model generalisation

Encode richer data.

Gain deeper insight from small datasets

Make more reliable forecasts.

Python SDK Improves Qode Engine Developer Control

Qode Engine provides developers and data scientists with a sophisticated Python SDK for quantum-enhanced corporate infrastructures. Even in beta, Qode Engine's infrastructure may develop from prototypes to production-grade solutions.

Benefits of Qode Engine

Advanced Quantum Algorithms

optimisation: Solve difficult optimisation issues with TetraOpt, TQoptimaX, ClearVu Analytics, QuEnc, and TQrouting.

TQml improves analytical depth and prediction accuracy.

Solid Python SDKs/APIs

Documentation and APIs simplify quantum model building.

Improve quantum models and integrate them into your Python routine quickly.

Hybrid, adaptable implementation

Mix CPUs, GPUs, and (eventually) Quantum Processing Units for best performance and cost.

Coordination with your existing infrastructure allows easy on-premises or cloud deployment (AWS, Azure, Google Cloud).

Framework Development for Security and Compliance

Due to its enterprise-grade architecture, Qode Engine meets security standards.

OIDC/OAuth2 authentication is supported by customisable RBAC and ReBAC.

Which SDK Libraries Exist?

The Qode Engine Python SDK supports TetraOpt, TQoptimaX, ClearVu Analytics, QuEnc, TQrouting, and TQml algorithms for optimisation and quantum machine learning.

Qode Engine works how?

Developers and data scientists may easily integrate quantum-enhanced activities into company infrastructures using a sophisticated Python SDK.

The normal Qode Engine process includes these steps:

Users can manually prepare or obtain data from their systems through data intake.

Model Development: The Python SDK designs, tests, and optimises quantum machine learning and optimisation workflows.

Solutions are performed using HPC resources such CPUs and GPUs, with some QPUs added later.

[thara333]

Charting the Course for Tomorrow's Testing Revolution: A Glimpse into the Future of Test Automation

In the ever-shifting landscape of software testing, the future is on the brink of a transformative revolution, driven by technological innovations and paradigm shifts. As we peer into the horizon of test automation, a realm of exciting developments unfolds, promising a testing landscape where adaptability and innovation take center stage.

1. The Rise of Smart Testing with AI and Machine Learning: The integration of Artificial Intelligence (AI) and Machine Learning (ML) into test automation marks a paradigm shift. Testing tools, infused with intelligence, will evolve to dynamically adapt, optimize scripts, and autonomously discern patterns. The era of intelligent and responsive test automation is on the horizon.

2. Embracing Shift-Left Testing Practices: The industry's commitment to a shift-left approach continues to gain momentum. Test automation seamlessly integrates into CI/CD pipelines, fostering swift feedback loops and hastening release cycles. Early testing in the development lifecycle transforms from a best practice into a fundamental aspect of agile methodologies.

3. Codeless Testing: Empowering Beyond Boundaries: Codeless testing tools take center stage, breaking down barriers and democratizing the testing process. Individuals with diverse technical backgrounds can actively contribute, fostering collaboration among developers, testers, and business stakeholders. The inclusivity of codeless testing reshapes the collaborative dynamics of testing efforts.

4. Navigating Production Realms with Shift-Right and Chaos Engineering: Test automation extends beyond development environments with a pronounced shift-right approach. Venturing into production environments, automation tools provide real-time insights, ensuring application reliability in live scenarios. Simultaneously, Chaos Engineering emerges as a pivotal practice, stress-testing system resiliency.

5. Unveiling Advanced Test Reporting and Analytics: Test automation tools evolve beyond mere script execution, offering enhanced reporting and advanced analytics. Deep insights into testing trends empower teams with actionable data, enriching decision-making processes and optimizing the overall testing lifecycle.

6. IoT and Mobile Testing Evolution: With the pervasive growth of the Internet of Things (IoT) and mobile applications, test automation frameworks adapt to meet the diverse testing needs of these platforms. Seamless integration with mobile and IoT devices becomes imperative, ensuring comprehensive test coverage across a spectrum of devices.

7. Hyper-Automation and Seamless Connectivity: The concept of hyper-automation takes center stage, amalgamating multiple automation technologies for advanced testing practices. Automation tools seamlessly connect with diverse ecosystems, creating a hyper-connected testing landscape. This interconnectedness ushers in a holistic approach, addressing the intricacies of modern software development.

8. The Imperative of Continuous Reskilling and Upskilling: Testers of the future embrace a culture of continuous learning. Proficiency in scripting languages, understanding AI and ML concepts, and staying updated with the latest testing methodologies become imperative. The ability to adapt and upskill becomes the linchpin of a successful testing career.

In conclusion, the future of test automation unveils a dynamic and revolutionary journey. Testers and organizations at the forefront of embracing emerging technologies and agile methodologies will not only navigate but thrive in this evolving landscape. As the synergy between human expertise and automation technologies deepens, the future promises to unlock unprecedented possibilities in the realm of software quality assurance.

[anilpal]

The Complexity of Multi-Modal AI Testing

Multi-modal systems introduce a unique blend of challenges:

Data Variability: Inputs can be natural language, gestures, audio, or images — sometimes all at once.

Non-Deterministic Outputs: AI-generated responses vary depending on input context and learned behavior.

Cross-Modality Interaction: A spoken command may trigger a visual result, which must be tested end-to-end.

Contextual Reasoning: Systems must process relationships between modalities in real time.

Traditional test automation simply can’t keep up. Genqe.ai reimagines testing with AI at its core.

How Genqe.ai Powers QA for Multi-Modal AI

Here’s how Genqe.ai addresses the complexities of testing multi-modal AI systems:

AI-Powered Test Generation for Multi-Modal Workflows

Genqe.ai automatically identifies and models real-world user flows across voice, text, image, and video interactions. For example:

Testing a virtual assistant that responds to both voice and visual cues

Ensuring accurate transcription + visual content delivery in e-learning tools

Validating gesture-to-command interpretation in smart devices

Tests are context-aware, scenario-driven, and self-maintaining.

Visual + Contextual Validation in One Platform

Multi-modal UIs are dynamic and often involve both content recognition and visual consistency. Genqe.ai combines:

Visual Regression Testing: Detect UI anomalies across devices and resolution changes

Contextual Testing: Validate that generated content matches expected context from prior modalities

For example, if a spoken query returns a data chart, Genqe.ai checks both the correctness of the chart and the alignment with the user query.

Self-Healing Tests Across Modalities

Multi-modal apps evolve rapidly. With Genqe.ai:

Broken test steps auto-heal using AI pattern recognition

Test cases adapt as AI model responses evolve

QA teams don’t need to rewrite test logic every time the UI or behavior shifts

This is key for systems that learn and improve over time.

API + Front-End Testing in Sync

Most multi-modal systems rely heavily on APIs and backend AI services. Genqe.ai ensures:

End-to-end coverage of API responses triggered by user actions

Synchronization between what’s processed in the backend and rendered to the user

Integrated validation of speech-to-text, image rendering, and content playback

All within a unified, low-code test environment.

Intelligent Reporting for AI-Driven Workflows

With Genqe.ai real-time dashboards and smart analytics:

Identify which modality is responsible for test failures

Prioritize test coverage based on user engagement trends

Track regression risk across voice, text, and visual layers

This insight-first QA helps teams build better, faster AI systems.

Key Advantages for Multi-Modal Testing with Genqe.ai

Real-World Example: Testing a Multi-Modal Health App

Imagine a user uploading an image of a skin rash, describing symptoms via voice, and receiving treatment suggestions visually. With Genqe.ai:

The image upload is validated against expected formats

Voice-to-text conversion is checked for accuracy

The diagnosis UI is verified visually and contextually

All backend API interactions are logged and tested in parallel

No scripting. No guesswork. Just smart automation, start to finish.

Conclusion: Genqe.ai is the Future of Multi-Modal QA

Testing AI systems that think and communicate across modalities requires a paradigm shift in QA. With Genqe.ai, you get:

AI-native, codeless testing

Multi-modal scenario coverage

Resilient automation with real-time insights

In 2025 and beyond, delivering intelligent user experiences starts with intelligent QA — powered by Genqe.ai.

[tonymattny]

Unlocking Simplicity: No Code Test Automation for 2025

Businesses Embrace Codeless Testing

No code test automation enables rapid validation of digital products. Testers execute complex scenarios without writing scripts. Modern platforms use visual workflows, making the process efficient. ideyaLabs leads to transforming traditional testing for growing digital needs.

Benefits Redefined with No Code Solutions

Team members collaborate without technical barriers. Non-developers build, maintain, and manage robust test cases. Scaling becomes simple as manual steps become automated. Projects transition from months to weeks. ideyaLabs solutions ensure enterprises meet tight delivery timelines.

Best Fit for Agile Workflows

Agile transformations boost speed across software development. No code test automation adapts perfectly to Agile sprints. ideyaLabs platforms deliver rapid updates, allowing for easy alignment. Teams maintain flexibility and achieve faster feedback loops.

Bridging the Gap: Empowering All Users

Collaboration thrives when everyone participates. UI-based automation opens test creation to QA, business analysts, and subject-matter experts. ideyaLabs software places power in every stakeholder's hands. Complex scenarios no longer require hand-coding experts.

Continuous Feedback for Quality Growth

Constant feedback forms the backbone of digital success. Automated tests run seamlessly in continuous integration pipelines. Teams find defects early and push releases with higher confidence. ideyaLabs provides dashboards for actionable insights.

Cost Management and Future Proofing

No code test automation shrinks overhead. Maintenance requires less technical skill, lowering costs. Businesses onboard new team members faster. ideyaLabs solutions adapt as requirements evolve, protecting investments with lasting relevance.

Accelerating Digital Transformation

Organizations digitize everything from workflows to customer experiences. Traditional testing slows the pace of innovation. No code automation gives companies a way to optimize processes. ideyaLabs builds tools that remove friction from digital growth.

Easy-To-Read Flow and Visual Workspaces

Visual workspaces make understanding test logic effortless. Flowcharts map out steps clearly. ideyaLabs gives testers drag-and-drop interfaces, speeding up case creation. Automation becomes visual, accessible, and transparent.

Eliminating Technical Bottlenecks

Coding complexity creates roadblocks for many teams. No code automation breaks down those barriers. ideyaLabs equips businesses with the tools to evolve without technical slowdowns. Every tester becomes productive immediately.

Rapid Bug Detection Boosts Confidence

No code test automation increases test coverage. Automated scripts catch issues before release. ideyaLabs solutions help identify problems early, ensuring higher quality for end-users. Continuous testing builds confidence in every product cycle.

Scaling Up Without Complications

Growing teams require scalable solutions. No code automation handles larger test suites and workflows. ideyaLabs platforms expand with businesses, supporting growth without heavy maintenance. Automation adapts, removing traditional ceilings.

Unmatched Flexibility for Evolving Products

Businesses change frequently, with new features rolling out every quarter. No code automation keeps pace with these updates. Testers add new scenarios without rewriting complex code. ideyaLabs gives businesses the agility to stay ahead.

Centralized Control for Teams

Effective teams need unified dashboards and collaboration tools. ideyaLabs centralizes test management and execution. Every stakeholder accesses up-to-date results from anywhere. Consistent performance becomes standard.

Data-Driven Decisions and Reporting

Automation generates detailed analytics and data. ideyaLabs platforms transform raw output into actionable reports. Managers review trends and optimize processes. Data becomes a key driver in shaping effective test strategies.

Security and Compliance at Every Step

Maintaining standards remains critical for regulated industries. No code test automation allows easy compliance checks. ideyaLabs ensures encrypted environments and robust access controls. Security measures stay in place during every phase.

Unlocking Fast ROI From Automation

No code test automation delivers return on investment sooner. Businesses see reduced manual effort and time savings. ideyaLabs ensures resource optimization through intelligent automation. Efficiency becomes measurable in weeks.

User Adoption Grows with Intuitive Designs

Onboarding new team members proves easier with no code tools. ideyaLabs offers intuitive layouts and guided workflows. Learning curves shorten for QA professionals and business testers alike. User satisfaction grows with seamless usage.

Integrating Seamlessly with Existing Systems

Compatibility matters for large organizations. ideyaLabs no code automation fits with various applications and tools. Teams automate without disrupting current workflows. Integration strengthens digital ecosystems.

Continuous Support and Community Engagement

Support ensures the continued success of automation initiatives. ideyaLabs provides active assistance and a vibrant community. Best practices circulate fast, helping businesses solve challenges collaboratively. The ecosystem grows stronger with every user.

Preparing for a No Code Future

Industries shift toward greater simplicity. No code test automation represents a paradigm change. ideyaLabs leads businesses into a world where automation works for everyone. Future-ready solutions remove complexity from innovation.

Start Transforming Testing with ideyaLabs

No code test automation brings smarter, faster, and more inclusive testing. ideyaLabs stands at the forefront, offering platforms tailored for today's enterprise needs. Unlock team potential and streamline digital transformation. Experience efficiency and agility in every phase.

[aisoftwaretesting]

Testing for Super Apps: New QA Strategies for Multi-Functional Platforms

Modernizing QA with Genqe.ai AI-Powered Testing

Super Apps — integrated platforms that combine services like payments, messaging, e-commerce, booking, and social engagement — have redefined the digital experience. With high complexity and immense user expectations, these apps demand a reimagined QA strategy to ensure quality, reliability, and rapid delivery.

In 2025, the key to success lies in intelligent, scalable, and codeless automation — and that’s exactly what Genqe.ai delivers.

The QA Challenge of Super Apps

Super Apps are not typical mobile apps. They’re multi-service ecosystems with features spanning across:

Financial transactions

Social media integrations

Real-time chat

On-demand services

Loyalty and rewards programs

This complexity introduces challenges such as:

Interdependent module testing

Continuous regression across frequently updated components

UI consistency across devices and brands

High-volume test coverage with limited time

Genqe.ai: Your Smart QA Co-Pilot for Super Apps

Genqe.ai is an AI-powered, codeless test automation platform designed to keep up with the ever-evolving architecture of Super Apps. Here’s how it transforms QA strategy:

AI-Powered Test Case Generation

Genqe.ai AI analyzes application flows and auto-generates test cases for even the most complex journeys. For a super app, this includes:

User onboarding across modules

Cross-feature transaction testing (e.g., booking and payment)

Context-aware journeys like “Chat while ordering”

It continuously updates test cases as the app evolves — no manual intervention required.

Unified Functional & Visual Testing

Super Apps must function seamlessly and look consistent across screen sizes, languages, and user types. Genqe.ai combines functional and AI-driven visual testing to:

Validate core logic and UI integrity together

Detect pixel-level mismatches

Ensure responsiveness across OS and device variants

The result: a cohesive user experience across all modules.

Scalable Cross-Module Regression Testing

With Genqe.ai, teams can execute parallel tests across integrated modules and services, ensuring that updates in one area don’t break another. This is critical for:

Shared authentication systems

Centralized wallets used across services

Notifications that trigger in multiple flows

Regression testing becomes smarter, faster, and fully autonomous.

API + Microfrontend Testing in One Flow

Super Apps are powered by complex APIs and sometimes microfrontend frameworks. Genqe.ai supports:

API testing directly within the same test scenarios

Seamless validation of data exchanges across microservices

Contract testing for service agreements

This ensures robust integrations behind the scenes — without separate test suites.

Real-Time Dashboards & Smart Reporting

Genqe.ai brings transparency to test execution with intuitive dashboards. QA leaders gain:

Instant visibility into test health across modules

Smart prioritization of failed tests

Heatmaps showing defect density across app areas

This data-driven insight accelerates quality decision-making.

Real-World Use Case: One Super App, Many Journeys

Imagine a user browsing products, making a payment, chatting with support, and booking a delivery — all in one session.

With Genqe.ai, QA teams can build and validate this entire experience as a single flow, fully automated and self-healing across environments.

Conclusion: QA for Super Apps Starts with Smart Automation

Super Apps demand next-generation QA tools — and Genqe.ai delivers the speed, scalability, and intelligence needed to support multi-functional platforms. With codeless automation, adaptive test generation, and deep integration support, Genqe.ai helps teams test faster, smarter, and more reliably in 2025.