• The Need for Intelligent Test Automation
• What is Tx-SmarTest? 
• Key Features of Tx-SmarTest 
• Business Benefits of Using Tx-SmarTest 
• Future-Proofing Your QA with AI and ML
• Summary 

The automation testing market is projected to reach $49.9 billion by 2026. This represents a fundamental shift in how enterprises are approaching software quality assurance. Businesses are investing heavily in AI and ML technologies to upscale intelligent test automation solutions where precision, quality, and agility are necessary. However, to support this, organizations will need a robust tool like Tx-SmarTest to facilitate faster releases enabled with DevOps CI/CD processes. Earlier, the testing process used to be slow and full of bugs because most QA tasks were handled manually. But today, automation and AI are leading the charge. 

This blog will discuss how Tx-SmarTest facilitates this shift and offers businesses a smarter test automation solution.

The Need for Intelligent Test Automation

As software systems become complex, traditional test automation struggles to keep pace with changing business requirements. Manual scripting, rigid test cases, and maintenance-heavy frameworks can slow release cycles and increase the risk of bugs in production. This is where intelligent test automation offers a smarter, more adaptive approach.

Intelligent automation integrates machine learning, predictive analytics, and self-healing capabilities to help teams test faster and more efficiently. It reduces manual effort and improves test coverage and accuracy. In today’s agile and DevOps-driven environments, it’s becoming a necessity.

Tx-SmarTest exemplifies this intelligent approach. It combines AI-driven components like the Accelerator for rapid code generation, the Migrator for seamless transitions, the Analyzer for in-depth insights, and the Predictor for forecasting potential defects. Together, these components enhance test coverage, reduce defect slippage, and streamline the automation process, making Tx-SmarTest a pivotal solution in modern test automation strategies.

What is Tx-SmarTest?

Tx-SmarTest is an AI-enabled, comprehensive platform developed by Tx to accelerate software testing and enhance quality. It seamlessly integrates with DevOps CI/CD pipelines, leveraging artificial intelligence (AI), machine learning (ML), deep learning, and neural network algorithms to identify edge test cases and automate them efficiently.

The platform comprises four core components:

• Accelerator: Expedites automation through code generation, documenting test cases, creating automation skeletons, and increasing test coverage to reduce defect slippage.
• Migrator: Facilitates seamless migration of key assets and automation scripts, ensuring continuity and efficiency in testing processes.
• Analyzer: Employs intelligent bug tracking to analyze and identify system errors, enhancing the accuracy of testing outcomes.
• Predictor: Utilizes predictive analytics to forecast potential defects, enabling proactive decision-making and risk mitigation.

Key Features of Tx-SmarTest

Tx-SmarTest stands out because it can integrate with various tools across the software development lifecycle. It connects with popular DevOps CI/CD systems, test management platforms, and defect tracking tools, ensuring that testing is not a siloed activity but a continuous part of development.

Another standout feature is its support for Tx-HyperAutomate frameworks, which enhances script reusability and allows for rapid automation scaling.

Additionally, Tx-SmarTest provides intuitive and role-based dashboards that deliver actionable insights across different levels of the organization, from QA teams to executive leadership. These dashboards simplify decision-making by offering real-time data on test results, defect trends, and automation progress. Together, these features make Tx-SmarTest a powerful solution for enterprises aiming to modernize their testing practices while maintaining speed and quality.

Business Benefits of Using Tx-SmarTest

Tx-SmarTest offers a wide range of business benefits that can significantly improve the efficiency and effectiveness of software testing. Accelerating automation through AI-driven code generation reduces the need for manual intervention, allowing teams to deploy test cases much faster. This boosts the overall speed of the testing process and accelerates time-to-market. Additionally, it helps discover potential impacts by analyzing data from the codebase and test assets. This ensures that testing efforts are focused on the most critical areas of the application, improving coverage and minimizing the chances of defects slipping through.

The accelerator also enhances error detection with intelligent bug tracking, enabling teams to identify system errors early in development. This reduces the cost and effort associated with fixing defects later on. Tx-SmarTest’s seamless migration capability ensures that organizations can smoothly transition their key test assets and automation scripts from older systems to more modern ones without disruption, saving time and resources.

Future-Proofing Your QA with AI and ML

Summary

Tx-SmarTest is an AI-powered test automation accelerator by Tx that uses UiPath Task Mining components to modernize quality assurance through predictive analytics, automated script generation, and seamless CI/CD integration. It addresses common challenges in traditional QA by reducing manual effort, increasing test coverage, and enabling faster releases. With core components like Accelerator, Migrator, Analyzer, and Predictor, Tx-SmarTest helps organizations achieve agile, efficient, and intelligent testing. Contact our experts now to know more about how Txs’ accelerators can help you upscale your QA initiatives.

The post Tx-SmarTest – Unlock Smarter QA with AI-Powered Platform first appeared on TestingXperts.

1. What is NG-TxHyperAutomate?
2. The Need for Low-Code Test Automation
3. Key Features of NG-TxHyperAutomate
4. Business Benefits of Implementing NG-TxHyperAutomate
5. Real-World Impact: How NG-TxHyperAutomate Improved Test Coverage by 90%
6. Summary

There was a time when enterprises depended on IT professionals to create scripts to keep their processes operational. Traditional QA methods often exceeded development time, as creating a new script takes much longer. However, enterprises must develop a cost-effective test strategy to balance speed and reliability to stay competitive in today’s AI-driven tech environment. That’s where low-code/no-code automation frameworks like NG-TxHyperAutomate come into play. These frameworks offer a better way to automate test scripts with minimal to no coding skills required. Even non-technical users can leverage such a platform to speed up the testing process.

With a goal of making the QA process faster and more reliable, enterprises are turning to AI-driven test automation to achieve better time to market without compromising on quality.

What is NG-TxHyperAutomate?

NG-TxHyperAutomate is an in-house low-code automation framework by Tx ACoE (Automation Center of Excellence) that assists QA teams in accelerating the test automation process. It easily integrates with tools like UiPath Test Suite, Katalon, and Tricentis Tosca, as well as CI/CD pipelines and test management systems like JIRA. The accelerator supports automation across web, mobile, desktop, and API platforms, enabling QA teams to reduce manual efforts and improve regression test coverage.

NG-TxHyperAutomate accelerates software QA processes, achieving faster time to market and maintaining quality standards. By integrating with existing tools and supporting a continuous testing approach, teams can adapt to changing user expectations and evolving business requirements.

The Need for Low-Code Test Automation

As digital transformation accelerators in the AI-driven ecosystem, enterprises must launch high-quality software more frequently and faster. Traditional test automation (like using tools and scripts, requiring coding knowledge) often requires deep technical expertise and time-consuming scripting. It slows down testing cycles and limits the chances of scalability. Low-code/no-code automation frameworks address these obstacles by enabling non-technical users to create test cases and making automation more accessible across teams.

NG-TxHyperAutomate in this space helps simplify and speed up the QA team’s automation processes. It generate automated scripts from manual test cases and offers reusable libraries and built-in support for localization. This helps reduce testing time and ensure flexibility, scalability, and alignment with changing business needs.

Key Features of NG-TxHyperAutomate

Low-Code/No-Code Automation: Enables faster test creation without deep coding knowledge.

Automate Test Generation: Automatically converts manual test cases into automation scripts.

Cross-Platform Coverage: One unified platform supports Web, Desktop, API, and Mobile automation.

CI/CD and Tool Integration: Seamlessly integrates with tools like UiPath Test Suite, Katalon, Tricentis Tosca, and systems like JIRA.

Globalization & Localization Support: Built-in capabilities to test software for multiple languages, regions, and cultures.

Continuous Testing Enablement: Facilitates ongoing validation throughout the software development lifecycle (SDLC).

Reusable Assets & Libraries: Upscale automation efficiency with prebuilt components.

Flexible Data Handling: Allows data to be processed directly from the SQL Server.

Adaptable to Evolving Requirements: Ensures tests align with shifting customer expectations and business needs.

Complex Scenario Handling with Robots: Uses robotic automation for intricate test workflows.

Business Benefits of Implementing NG-TxHyperAutomate

Accelerated Time-to-Market:

By automating test case generation and execution rapidly through low-code/no-code workflows, teams can release software faster and more frequently without degrading quality.

Decreased Testing Costs:

Less manual effort, fewer resources required, and prebuilt automation libraries reduce overall testing and QA-related costs.

Improved Test Coverage and Reliability:

With up to 96% regression coverage and built-in test generation, businesses gain confidence in their releases and minimize post-deployment issues.

Scalable Automation Across Platforms:

A single framework supports Web, Mobile, API, and Desktop testing, enabling consistent automation practices across diverse application ecosystems.

Better Integration with Agile and DevOps:

Integration with CI/CD pipelines, test management tools, and defect tracking systems supports agile development cycles and continuous delivery goals.

Optimized Flexibility and Adaptability:

The system easily accommodates new features, evolving requirements, and third-party integrations, making it ideal for dynamic, enterprise-grade environments.

Lower Maintenance Costs:

Automated maintenance and reusable components help keep test suites up to date with minimal effort as applications evolve.

Risk Mitigation via Early Bug Detection:

Continuous testing throughout the development lifecycle enables earlier defect detection, reducing costly downstream fixes.

Support for Global Expansion:

Built-in localization and globalization testing ensure products are validated for multiple languages and regions, supporting international growth.

Real-World Impact: How NG-TxHyperAutomate Improved Test Coverage by 90%

A major quick-service restaurant chain leveraged NG-TxHyperAutomate to modernize its POS systems globally. Facing bottlenecks due to prolonged manual testing cycles and complex localization needs, the client implemented NG-TxHyperAutomate using UiPath Test Suite alongside additional Tx accelerators. This integration enabled low-code automation, significantly improving deployment efficiency. It helped the client achieve a 150% increase in POS rollout speed, 93% regression test automation, and a 90% boost in overall test coverage, delivering over $1.2 million in QA cost savings.

Summary

Traditional QA approaches struggle to meet the demands of modern software delivery, where speed, scale, and adaptability are critical. NG-TxHyperAutomate addresses this gap with its low-code automation framework that simplifies complex testing processes while enhancing accuracy and coverage. Its ability to support multiple platforms, adapt to localization needs, and integrate into CI/CD pipelines makes it a strategic enabler for quality at speed.

Partnering with Tx for NG-TxHyperAutomate will help you scale automation across platforms, adapt quickly to changing business needs, and optimize your DevOps workflows. Contact our experts now to learn more about how our accelerators can help you optimize your QA processes.

The post Unlock Seamless Test Automation and Drive Quality with NG-TxHyperAutomate first appeared on TestingXperts.

1. What is NG-TxAutomate?
2. Why Businesses Need a Test Automation Framework?
3. Challenges Addressed by NG-TxAutomate
4. Key Features of NG-TxAutomate
5. Summary

It hasn’t been too long since the world witnessed the magic of the Metaverse at the Sandbox’s first Global Creators’ Day on November 3, 2023. As technology expands, enterprises will need more robust digital assurance and automation solutions to survive the era of AI transformation. There’s no denying that Artificial Intelligence (AI) and Machine Learning (ML) are upscaling testing strategies, facilitating the new era of innovation and efficiency.

However, various QA managers or test engineers still hesitate to adopt next-gen technologies to upscale automated testing due to a change in knowledge base and high upfront cost. To overcome such challenges, Tx has an AI-powered framework, NG-TxAutomate, that transforms the quality assurance process and delivers significant QA cost savings.

What is NG-TxAutomate?

NG-TxAutomate is an AI-powered and intelligent “ready-to-deploy” test automation framework by Tx ACoE (Automation Center of Excellence). It is a modular, reusable, compatible, and library-agnostic automation framework that can accelerate the QA process in Agile and DevOps environments. This framework can easily integrate with tools like JIRA, Selenium, Playwright, Jenkins, etc., to support automation efforts across web, mobile, and API platforms. It leverages AI to improve software testing cycles’ accuracy, productivity, and efficiency. Using NG-TxAutomate, enterprises can reduce testing cycle times, lower QA costs, and deliver high-quality software quickly.

QA teams can easily integrate NG-TxAutomate’s AI features into their workflow by adding a plugin to their preferred IDE (Visual Studio Code, IntelliJ IDEA, or another tool). This smooth integration lets them access powerful AI-driven automation within their development environment, boosting productivity and testing efficiency.

Why Businesses Need a Test Automation Framework?

A test automation framework enables businesses to improve software QA processes’ speed, accuracy, and efficiency. It helps deliver faster time to market, improved product quality, and reduced QA costs by automating redundant tasks and enabling teams to focus on complex and AI-driven QA activities. Here’s how it benefits businesses in the long run:

• Automated test cycles enable teams to execute many test cases, including edge cases missed in manual testing.

• Frees up QA engineers from repetitive tasks and gives them time to focus on next-gen testing strategies and analysis.

• A robust framework like NG-TxAutomate significantly reduces the cost of maintaining and updating test scripts.

• Automation framework integrates seamlessly with CI/CD pipelines, enabling continuous testing of code updates throughout the SDLC and rapid deployment.

• Ensuring output consistency across various platforms and environments enables QA teams to execute the same testing steps precisely every time.

Challenges Addressed by NG-TxAutomate 

Key Features of NG-TxAutomate

Speed up QA Efforts Across Platforms:

Run test cases smoothly on web, mobile, and API interfaces without switching between tools.

Easy to Integrate with Tools:

Work efficiently with systems like Playwright, JIRA, Selenium, Azure DevOps, Jenkins, and Cypress without extra setup (hardware/software).

Reduce Repetitive Work:

Teams can reuse test components and scripts, allowing them to spend less time rewriting the same steps.

Catch Bugs Early and Often:

Integrate automated test cases into the software development flow to facilitate quick issue identification.

AI-Powered Capabilities:

AI-driven code generation allows teams to decrease dependency on coding everything from scratch and support extensive reporting via AI insights and predictive models.

Hybrid Testing Support:

Run both functional and non-functional tests across multiple environments to ensure full QA coverage.

Integration with Tx Products:

Easy to integrate with tools like Tx-SmarTest, Tx-Insights, and Tx-GPT to prioritize AI-driven testing, track real-time outputs, and auto-generate test cases from user stories.

Multi-Language Support:

Teams can work with multiple languages, such as Java, C#, Python, and JavaScript, making it easier to build, customize, and maintain test scripts.

Real-world Impact: How NG-TxAutomate Cut Testing Efforts by 90%

In a notable project, Tx worked with a leading commercial insurance provider who was migrating from the legacy systems to the Majesco Insurance Platform. During migration, the client encountered several challenges validating domain-specific functionalities and ensuring application stability in the SIT environment. Tx leveraged NG-TxAutomate, its in-house automation framework, to address these issues and streamline the QA process. It led to a 90% reduction in testing efforts and a 30% increase in test execution speed. The integration of NG-TxAutomate enhanced testing efficiency and ensured functional stability during the platform migration, demonstrating its effectiveness in complex enterprise environments.

Summary

As enterprises race to keep up with the AI transformation, the pressure to deliver faster, smarter, and more reliable software is higher. Yet many enterprises still struggle with outdated tools, steep automation learning curves, and rising costs. NG-TxAutomate, an AI-powered, ready-to-deploy framework from Tx, helps streamline testing, enabling early automation and effortlessly integrating across platforms and tools. With NG-TxAutomate, organizations can confidently adopt digital agility while minimizing QA bottlenecks and boosting release speed.

The post From Weeks to Days – How NG-TxAutomate Shrinks Automation Timelines first appeared on TestingXperts.

Enterprises across industries continue to demand faster releases and quality software to withstand the ever-changing market scenario and competition.

With enterprises following DevOps and agile methodologies of software development, software testing forms an integral part of it. The earlier manual method of testing does not suffice the present-day connected devices and complicated architecture applications with industry 4.0 in place.

Table of Contents

1. What is Automation Testing?
2. What does Test Automation Do?
3. Significance of Automation in Testing for Enterprises?
4. Best practices of Test Automation
5. Benefits of Automation Testing
6. How does Test Automation works?
7. What Does an Automation Tester Do?
8. What are Automation testing challenges and how to overcome them?
9. Automation Testing Vs Unit Testing
10. Why Should You Leverage Next-gen Testing Services Provider for Test Automation?
11. Related Queries on Automation Testing

There is a need for businesses to adopt test automation that ensures faster and quality releases along with saving considerable time, and efforts.

What is Automation Testing?

According to WIKI, automation testing is the use of software separate from the software being tested to control the execution of tests and the comparison of actual outcomes with expected outcomes.

Test automation can automate some repetitive but necessary tasks in a formalized testing process already in place, or perform additional testing that might be difficult to do manually.

Test automation can speed up the release cycles, increase coverage and ensures quality software releases.

In manual testing, a human is involved to perform the manual tests that are more prone to human error and might even lack their correctness. This sort of human involved testing encloses many gaps which are seen and sometimes even have missed project timelines due to the huge number of misidentified bugs.

But, as automation testing involves the usage of tools and test scripts to test the software, the results are more reliable and delivers accurate results while testing various use cases and user scenarios.

Hence, this sort of automation testing ensures effective outcomes and can be successfully used by businesses to ensure faster and quality releases.

Automated testing is also known as test automation is a method in software testing that effectively makes use of special test automation tools. These tools are used to control the execution of tests using well-crafted test cases for each of the user scenarios and compares the actual test results to the predicted or the expected results.

It is actually a process that validates if the software is functioning appropriately and meeting the given requirements using these test automation tools.

What does Test Automation Do?

Automation testing meaning can be broadly defined as the process which involves running scripts that are executed by leveraging software testing tools. Automated test systems ensure high accuracy and reliability of the test results.

As no human observations are involved, it ensures more accurate test results. Moreover, this sort of automation testing using open source and paid test automation tools is most suitable for regression testing, performance testing, load testing, security testing along with other types of testing.

Significance of Automation in Testing for Enterprises?

Software testing is essentially an important part of the software development life cycle (SDLC) and should be taken up to ensure quality software.

Earlier manual testing was the major mode of testing but with advanced applications and connected devices in place, it is essential to have Test Automation to ensure faster and quality releases.

Even smaller and mid-sized companies in their quest to have good quality products continue to adopt test automation best practices to deliver high-performing products to their clients.

For enterprises, with automation in testing in place, there is a faster feedback cycle and that brings faster validation at various phases in product development.

The other benefit of automation testing is the testing procedure becomes automated as the test scripts are run by the test automation tools without much interference of QA.

Test automation also helps enterprises to improve the communication between DevOps, QA teams and product owners who depend on the results of these automated tests as these results are more reliable than the manual testing results.

It is more important to note that automation testing is best suitable for applications that need to be tested for various versions, platforms, and operating systems.

Test automation involves the usage of tests that are repeatable and can be run many times to test various conditions and scenarios.

It is best suited with regression tests and high-risk functionality tests to check whether they fail under real-time conditions.

These automated test results help to save businesses of any probable loss due to their application failure as with effective automated software testing the results are more reliable.

Best practices of Test Automation

Below listed are 6 best practices for test automation:

1. Select the right automation tool:

Testing teams must select the right automation testing tool that is in-line with their project requirements which helps them to execute automated test cases in an effective manner.

2. Design tests before automating them:

It’s always preferred and is considered as a good practice to create test cases before initiating the test automation process. A good test design will eventually help in finding defects and fixing them.

3. Don’t plan to automate unstable functionality:

Whenever there is a new feature or functionality that is to be tested, care should be taken such that automation testing will be taken up only for stable functionalities.

4. Decide which tests to automate:

Every feature cannot be automated as there are certain features that require manual testing. A proper automation test strategy needs to be put in place to fulfil all those test cases that need to be automated.

5. Early and frequent testing:

To leverage test automation, it is much better to start testing early on in the software development process. By running automated tests often, testers will be in a better position to detect bugs and resolve them at the earliest. This would save time and money which would have been spent otherwise on fixing bugs at a later stage of the software development process.

6. Do not completely rely on UI test automation:

Completely relying on UI test automation is not considered as a best practice as every application might have some UI changes made. Hence, UI testing should be planned carefully to ensure the app delivers a great user experience to end-users.

Benefits of Automation Testing

There are many benefits with Test Automation which have been broadly classified as Qualitative and Quantitative benefits.

Qualitative Benefits of Automation in Testing

– Speeds up the testing cycle with automated runs

– Reduces test execution time effectively

– Increases test coverage on each testing cycle

– Ensures ease of test case maintenance

– Reduces the need to update scripts in case of changes to the application

– Ensures faster test scripts generation

– Facilitates reusability of code

– Reduces manual intervention and they can be utilized for functional testing

– Reduces testing timelines significantly

– Reduces regression testing time enormously

– Saves time as automated testing brings in more efficiency

– Improves test accuracy compared to manual testing

– Breaks up large business processes into smaller components to be re-used multiple times

– Ensures longer automation code life

– Increases the team’s testing speed

Quantitative Benefits of Automation in Testing

– Generates quicker ROI

– Reduces QA cycle times by up to 80%

– Lowers the overall QA costs up to 60%

– Improves organizational efficiency

– Ensures cost optimization

– Delivers faster time to market

– Helps improve the business bottom line

After having known about the various benefits of Automation testing, it is necessary to know about the different types of automation testing.

What are the Types of Automation Testing?

Unit Testing:

This is the first type of automation testing in which the individual components of an application are tested. These tests are usually written by the developers but sometimes automation testers also write them. These tests are usually performed during the software development phase.

Smoke testing:

This type of testing is performed to check whether the build is stable or not. If the build is not stable, then it will be sent back to the developers as an unstable build and if the build is stable then testers will accept it to perform further testing on the build.

Black box testing:

This form of testing is performed to check if there are any incorrect or missing functions, checks for the existence of any interface errors, or any behaviour related or performance errors and also checks the presence of termination errors if any.

Integration testing:

In this integration testing, the application modules are typically integrated logically and tested as a group. Essentially, this form of testing checks and verifies the data communication between different modules of the application.

Functional testing:

This form of testing is done to check if all the functions work as expected or not. This involves testing APIs, database, security testing and also tests the overall functionality of the application.

Regression testing:

This is an exhaustive testing methodology done to check whether all the existing features are functioning well even with recent code changes.

It basically verifies that the old code works in the same way as they were before making any changes to the code.

Some of the Most commonly used Test Automation tools are:

Below is the automation testing tools list that will help you understand more about each of these tools

Selenium:

Selenium is one of the most popular test automation tool and is the preferred tool for web automation testing for DevOps. Selenium offers two parts of its software, Web driver and IDE (Integrated Development Environment). Selenium automation testing is also one of the most preferred automation testing in the market these days.

Appium:

It is an open-source test automation framework that can be used with native, hybrid and mobile web applications to test them.

It facilitates to make use of preferred test practices, frameworks, and tools. It is preferred to automate any mobile app from any language and any test framework.

Cucumber:

It is an Open source automation testing tool that revolves around behavior-driven development.  

Ranorex:

This is a unique test automation tool that can be used to test web apps, desktop or mobile applications.

TestComplete:

This is the first automation testing tool that has AI with a visual recognition engine that can test with or without scripts and the tool can be used to test desktop, mobile, and web applications extensively.

Microfocus UFT:

The Unified Functional testing tool (UFT Tool) is used in regressive and functional test automation.

It also supports API and web services testing, continuous testing, support for testing ERP and CRM applications.

Apache JMeter:

This is the most popular performance testing tool that perfectly measures the performance of your software. It is built on java and runs functional tests to measure the performance of web applications.

Tosca:

This is a very effective testing tool that can be used for end-to-end testing as it supports functional testing, load testing, risk-based testing, test data management and also app testing.

How does Test Automation works?

10 Basic Steps for a Well-Defined Test Automation

1. Evaluate to understand the need for automation based on project type

2. Define automation goals and priorities

3. Plan automated testing strategy

4. Select the right automation tool & framework based upon the project requirements

5. Decide on what test cases to automate

6. Develop good quality test data

7. Create automated tests more resistant to UI changes

8. Execute the tests for the test scripts developed

9. Test early and often with CI/CD pattern

10. Maintain the test scripts

Business Benefits with a well-defined Test Automation Approach

– Delivers quality software products

– Increases release velocity through the CI/CD model

– Reduces human errors as human tasks are automated

– Ensures cost optimization

– Maximizes ROI

– Ensures faster time to market

What Does an Automation Tester Do?

The QA automation tester and the automation test engineers are the main people involved to achieve test automation initiatives. They know how to automate for the successful roll-out of quality products for businesses.

The QA automation tester is responsible for designing and writing test automation scripts as the first phase and they use various types of test automation frameworks to achieve successful test automation.

They run test scripts to ensure all the scenarios are successfully tested and they have a close association with the developers and other QA analysts.

They log bugs and issues in various test management tools such that the developers work on these bugs and fix them.

Once again automated regression testing is done to ensure the new functionalities do not affect the functionalities of existing features.

What are Automation testing challenges and how to overcome them?

Though Test automation brings about a lot of advantages for enterprises. Typically, by using automated testing, an expedition of the process of software validation can be achieved along with achieving increased test coverage.

However, there are a lot of challenges in applying test automation for applications under test (AUT) which need a careful follow-up process to achieve successful test automation implementation.

1. Lack of Proper communication & collaboration between Developer and QA teams: 

Lack of proper interaction and collaboration between developers, QA automation engineers, and operations teams does not signify test automation success.

Hence, it is essential to place the entire team members to get involved in identifying test automation objectives and setting targets.

They also need to spend significant efforts on proper communication between teams to have a clear understanding of the requirements and project specifications to ensure test automation success.

2. Proper Selection of Right Test Automation Tool:

With the availability of a variety of automation testing tools for both open source and paid tools, it is necessary to evaluate properly before adopting a tool. A proper in-depth evaluation of the application under test (AUT) is required.

It should be carefully done and then the tool that appropriately matches the AUT should be chosen.

To overcome it, an expert QA test automation engineer can prove helpful to evaluate the tools based on the application that has to be tested.

3. Lack of Highly Skilled Test Automation Experts:

It is a truth that test automation requires efficient and industry rich technical skills to accurately design and maintain test automation framework and test scripts.

The team should properly build test scripts and maintain them to effectively run test scripts to verify the application performance and further resolve technical issues.

In order to overcome it, automated testing resource(s) should have a strong knowledge of the Test Automation framework’s design and implementation aspects to ensure effective automation is achieved.

4. Involves High Initial Investment Costs:

It is also a known fact that the initial phase of test automation is usually expensive as it is necessary to analyze, design and build a automation testing framework, libraries or reusable functions through a thorough analysis of the AUT.

Further, if a licensed test automation tool is selected, then the operating cost should be evaluated.

Else, if free open-source tools are chosen then also significant efforts should be spent on learning, training and maintaining them.

Hence based on the AUT priority and cost capability available therein, either open source or licensed tools should be selected.

5. Challenges with Scaling Test environments:

This is an important challenge today as QA teams that don’t provision test environments in the cloud, continue to face scalability challenges when they introduce test automation.

It is necessary to rapidly provision the different test environments that automated testing requires, scale them up, run the tests, tear them down, to ensure success while testing in the cloud.

The in-house (on-premise) teams usually have a limited number of test environments they can use, which means fewer tests they can deploy at any given time.

As a result, testing takes a much longer time. In order to overcome this, it is essential to move test environments to the cloud to scale the test automation and it will also reduce QA teams operating costs.

Automation Testing Vs Unit Testing

Why Should You Leverage Next-gen Testing Services Provider for Test Automation?

Test automation is nowadays dominating in the Agile and DevOps adoptions. Test automation enables tremendous benefits to enterprises as it ensures faster releases, quality software and also delivers a quicker time to market.

But, in order to achieve successful test automation for all critical projects, it is essential to leverage next-gen automation testing services providers as they use a balanced test automation approach to achieve quality products.

Some of the Highlights of the Approach they perform are:

– Assessment of your automation needs and application landscape

– Assessment of your existing automation solution

– Tool selection, Proof of Concept and implementation

– Automated test script creation and execution

– Creation of regression test suites with flexible execution options

– Training and handover of automation test pack to client for future maintenance

Moreover, with the huge number of benefits with automation testing services, it is time for enterprises to adopt test automation initiatives using either open source or the licensed automation testing tools.

TestingXperts has developed an extensible test automation framework, ‘Tx-Automate’, which is modular, reusable, integrated and compatible.

The framework has best-in-class features for test automation including rich custom reporting metrics, third party integrations, configurable execution options, etc.

The framework helps configure/ create test suites by combining various automated tests and making those test suites available for execution.

Get in touch with our team to discuss further

The post A Beginner’s Guide to Test Automation 2025 first appeared on TestingXperts.

1. What is App Modernization?
2. Types of App Modernization
3. Role of Automated Testing in App Modernization
4. Automation Testing Tools & Frameworks for App Modernization
5. How to Choose the Right Automated Testing Tool?
6. Conclusion
7. How Can TestingXperts Help with Automated Testing for App Modernization?

What is App Modernization?

App Modernization refers to updating older software applications with modern architectures, features, and capabilities, ensuring they remain relevant and efficient in contemporary IT environments. It encompasses many activities, from simple code refactoring to replacing legacy systems entirely.

The digital landscape is evolving at a remarkable rate. With over 87% of enterprises reporting digital disruptions challenging their business models, there’s a pressing need to adapt and transform. Here’s why modernization is so essential:

Stay Competitive:

Modern applications cater to current market expectations, ensuring businesses remain relevant in the eyes of customers and stakeholders. According to a study, companies that fail to modernize risk falling 20% behind their competitors in revenue and customer satisfaction.

Optimize Costs:

Legacy systems, although familiar, can be expensive to maintain. Upkeeping outdated software drains resources, while modern apps can reduce operational costs by up to 25%, enhancing ROI.

Scalability & Flexibility:

Modern applications are designed to scale with growing business demands. They offer flexibility, ensuring businesses can quickly pivot in response to changing market dynamics.

Enhanced Security:

As cyber threats become increasingly sophisticated, outdated software becomes more vulnerable. Modernized apps incorporate the latest security measures, protecting valuable business data from breaches.

Types of App Modernization

App modernization is a comprehensive process that involves updating and improving software applications to enhance performance, security, and adaptability in today’s technological landscape. As technologies evolve, businesses need various methods to update their software based on specific needs and objectives. Let’s explore the different modernization techniques to determine which might be optimal for varying scenarios:

Re-hosting & Re-platforming:

Re-hosting is about taking an app from one environment and moving it to another, like from an in-house server to the cloud. It doesn’t change how the app looks or works but where it’s located. Re-platforming means making changes for apps to perform better in their new environment without altering their core functions.

Refactoring & Re-architecting:

Refactoring is making small changes in the software to improve its performance or clarity without changing what it does. Re-architecting means restructuring the app, changing how it works internally to make it more efficient or to add new features.

Rebuilding & Replacing:

Rebuilding means making the app again from scratch using modern methods and technologies. Replacing means buying or adopting a new app to replace the outdated one.

Every business is different, and there’s no one-size-fits-all answer. When deciding on a modernization approach, companies should consider their goals, budget, and how crucial the app is to their operations. For some, a simple re-host might be enough. Others might benefit from a complete rebuild. It is essential to evaluate the needs, consult with IT experts, and choose a path that aligns with the business’s future vision.

Automated Testing: A Deeper Dive

In the context of app modernization, the efficacy of the updated software is paramount. Automated Testing, an integral aspect of modern software development, employs specialized tools and software for comprehensive testing procedures. This approach not only assures the quality of the software but also streamlines the deployment process.

Why Manual Testing Isn’t Enough in Modernization?

While manual testing offers the advantage of human intuition, its scalability and efficiency in the face of complex modernized applications are limited. Applications today can encompass extensive lines of code, making exhaustive manual testing cumbersome and less precise. Automated Testing provides a systematic and swift alternative, enabling thorough verification of modernized applications to ensure optimal performance.

Continuous Integration and Continuous Deployment in Modernization

CI/CD displays modern software development characteristics. CI represents the frequent integration and testing of code changes, while CD ensures that these validated changes are promptly deployed. Automated Testing is foundational to this framework, facilitating rapid and reliable assessments and enabling businesses to deploy updates with greater frequency and confidence.

Role of Automated Testing in App Modernization

Ensuring the new version works perfectly when updating old software is crucial. That’s where Automated Testing comes in. It checks every part of the software to ensure it’s functioning correctly and is secure. Let’s explore how Automated Testing helps when modernizing apps:

Ensuring Seamless Transition:

When businesses update their software, they must test it to ensure a smooth transition. Automated Testing does this quickly and thoroughly. It checks the new software, finds problems, and ensures that the transition from old to new is smooth, with no major errors.

Reducing Errors and Reworks:

Mistakes can be expensive. If a business launches updated software with issues, fixing them later can cost time and money. Automated testing detects these issues early on. By catching problems before they reach users, businesses can save money on fixing issues and avoid the costs of unhappy customers.

Improving Time-to-Market:

In the business world, time is money. Getting updated software to customers faster can be a significant advantage. Automated Testing speeds things up. Instead of waiting for humans to check everything manually, a computer program quickly tests the software. This means businesses can launch their modernized apps faster and stay ahead of the competition.

Building Trust in Modernized Apps:

People trust brands that offer quality. When businesses update their software, they want users to trust the new version. Automated Testing helps with this. Thoroughly checking the software ensures that the updated app is of high quality. So, when users use it, they can be confident that it will work well.

Automation Testing Tools & Frameworks for App Modernization

As app modernization becomes necessary, the right automated testing tools and frameworks become essential to ensure the revamped software meets quality standards. These tools simplify the testing process and seamlessly integrate with modern development practices, making them a vital aspect of the modernization process. Here’s a list of some of the automated testing tools:

Selenium:

Selenium, an open-source tool, has gained popularity among testers for its flexibility and wide range of capabilities. It allows testing of web applications across various browsers, ensuring that the modernized application performs uniformly across platforms. The tool’s versatility, backed by a supportive community and rich documentation, makes it an ideal choice for many businesses venturing into app modernization.

Appium:

With the surge in mobile app usage, testing mobile apps for functionality and user experience is crucial. Appium is an open-source platform that enables automated testing on iOS and Android platforms, regardless of the native, mobile-web, or hybrid application type. Its ability to write tests in multiple programming languages further underscores its adaptability and broad reach.

TestNG & JUnit:

Frameworks like TestNG and JUnit have reshaped how developers approach testing in the Java environment. TestNG, inspired by JUnit, offers enhanced functionalities like grouping, sequencing, and parallelizing tests, ensuring a structured and comprehensive testing process. On the other hand, JUnit is known for its simplicity and annotations-driven approach, making it a staple in many modernized application testing scenarios.

Jenkins:

It’s an open-source tool that facilitates the automation of various stages in the development pipeline, including testing. Integrating seamlessly with various testing tools, Jenkins accelerates the feedback loop, allowing developers to address real-time issues essential for apps undergoing modernization.

How to Choose the Right Automated Testing Tool?

The key to making an informed choice lies in aligning the tool’s capabilities with the project’s specific requirements. This section provides a roadmap for businesses to determine which tool best aligns with their app modernization objectives.

Understanding Application’s Architecture:

Before starting with tool selection, it’s crucial to understand the application’s architecture undergoing modernizing. Is it a web application, a mobile app, or a desktop application? Different tools are optimized for different platforms. For instance, while Selenium excels in web application testing, Appium is tailored for mobile environments.

Integration with Existing Systems:

Consider how well the testing tool integrates with existing systems. A tool seamlessly integrating with your current development environment, version control system, and CI/CD pipeline can streamline the testing process, reduce manual interventions, and increase efficiency.

Flexibility and Scalability:

Modernized applications often evolve, adding new features and functionalities. The chosen testing tool should be flexible and scalable to accommodate this growth. It should allow testers to quickly modify, expand, or reduce tests’ scope without substantial rework.

Language and Framework Support:

The programming languages and frameworks the team is familiar with should be considered significantly. If the team is well-versed in Java, tools that support Java will have a shorter learning curve. Similarly, if an app is built on a specific framework, ensuring the tool that supports that framework can be beneficial.

Cost Implications:

While open-source tools come without licensing fees, it’s essential to consider the total cost of ownership. It includes training, maintenance, integration, and potential downtime costs. Sometimes, investing in a paid tool with premium support and features can be more cost-effective in the long run.

Performance and Reporting Capabilities:

Real-time feedback and comprehensive reports are vital to understanding the efficacy of tests. A tool that provides detailed logs, visual reports, and real-time alerts can help teams quickly identify issues and improve the quality of the modernized app.

Conclusion

The digital realm constantly evolves, with innovations and technologies reshaping how businesses operate and deliver value. In this dynamic environment, app modernization has become necessary for any organization to stay relevant and competitive. With its efficiency and precision, automated testing is essential for businesses. Businesses can confidently leverage new opportunities and tackle emerging challenges by ensuring modernized apps function optimally and meet quality standards.

How Can TestingXperts Help with Automated Testing for App Modernization?

In the evolving digital landscape, app modernization helps businesses to progress, offering them a way to remain agile, relevant, and ahead of the competition. Automated testing ensures that revamped applications meet and exceed quality benchmarks. At TestingXperts, we deliver top-notch automated testing solutions tailored for app modernization, ensuring your software is robust, efficient, and future-ready. Here’s why you should partner with TestingXperts for app modernization services:

• We utilize cutting-edge tools and methodologies for accurate testing

• Comprehensive testing expertise across web, mobile, and desktop platforms

• Seamless integration with continuous integration and continuous deployment pipelines

• Tailored solutions aligning with specific app modernization objectives

• Proactive monitoring and real-time feedback for early issue identification

• Commitment to continuous learning, ensuring alignment with the latest trends and best practices

• To know more, contact our QA experts now.

The post Importance of Automated Testing in App Modernization first appeared on TestingXperts.

The manufacturing industry hasn’t been left untouched by the industry 4.0 revolution. It is driven by the need for efficiency, adaptability, and precision. Unlike traditional automation, which lacks the flexibility to handle complex processes, intelligent automation (IA) in the manufacturing industry optimizes operations, enables data-driven real-time decision-making, and reduces costs. However, manufacturers must implement cutting-edge solutions like AI, ML, IoT, and RPA to fully leverage IA benefits. From predictive maintenance to real-time quality control, intelligent automation is reshaping the manufacturing industry.

This blog will explore how intelligent automation transforms the manufacturing sector, the key technologies involved, and why businesses should leverage it for long-term growth.

Intelligent Automation in Manufacturing Industry

After the era of industrial automation, intelligent automation is transforming the manufacturing sector with advanced automation technologies like LPA, RPA, and AI. It allows manufacturers to focus more on innovation and core tasks. They can free their workforce from repetitive processes and can achieve approx. 40% reduction in operational costs, optimize performance, improve quality, and facilitate process automation.

Intelligent automation optimizes production lines and minimizes downtime to ensure consistent product quality. Predictive maintenance, one aspect of IA, prevents failures before they penetrate the production cycle. Also, IA-driven systems help improve supply chain management by supporting inventory management, logistics, and demand forecasting. Manufacturers can achieve higher productivity, improve agility, and reduce costs, a critical factor in today’s digital and AI-enabled industry.

IA Technologies Transforming the Manufacturing Sector

Intelligent automation integrates advanced technologies affecting manufacturing process efficiency, scalability, and precision. Let’s take a quick look at the technologies driving this transformation:

AI-driven Robotics and Autonomous Systems:

Modern autonomous and robotic systems powered by AI can perform complex tasks like precision assembly, adaptive manufacturing, collaborative operations, material handling, etc. They can quickly adapt to the working environment and collaborate with human workers to improve operational efficiency. The robots can be more productive in handling assembly lines, product handling, and quality inspection.

Internet of Things (IoT):

Integrating IoT sensors with machines and devices streamlines data flow, enabling manufacturers to generate real-time data for monitoring and decision-making. This helps with predictive maintenance, remote asset management, and process optimization.

Machine Learning:

ML algorithms use historical and real-time data to predict the likeliness of software and hardware failures. This helps reduce downtime issues, improve the lifespan of assets, and keep equipment maintenance costs in check.

Intelligent Machine Vision Systems:

Manufacturers can automate quality control inspections using AI-powered vision systems. These systems help analyze product quality with high precision and detect defects that are hard to spot with human eyes. These systems use computer vision and AI to improve QA and reduce production line waste.

Digital Twins:

In the manufacturing industry, a digital twin can imitate the virtual replica of a physical process, enabling businesses to simulate operations, optimize test runs, and predict results before change implementation. This technology helps minimize risks and improve efficiency.

Benefits of Intelligent Automation in Manufacturing

By implementing IA in the manufacturing sector, businesses can optimize their inventory management and maintenance activities and improve their efficiency, productivity, and sustainability. Let’s take a close look at some of the business benefits of leveraging intelligent automation in the manufacturing industry:

Supply Chain Optimization:

Intelligent automation will ensure seamless real-time inventory tracking, demand forecasting, order processing, and vendor communication. This will enable the seamless movement of raw and finished products through the supply chain. Manufacturers can reduce bottlenecks, improve delivery timelines, and avoid stock-related issues.

Compliance Management:

Simply the regulatory adherence process by automating documentation and compliance monitoring and ensuring the process aligns with strict industry regulations and quality standards. This would help reduce violations and penalties risk.

Digital Platforms Connectivity:

Modern manufacturing infrastructure relies on interconnected systems like IoT-enabled machines and cloud-based platforms. IA enables uninterrupted cross-platform data exchange, enabling real-time insights, predictive analytics, and remote monitoring. This helps improve business decision-making and efficiency.

Facilitate Innovation:

IA can handle data-intensive and repetitive tasks, enabling enterprises to redirect their resources towards R&D and innovation. AI-driven simulations and digital twins can accelerate product development, leading to faster time-to-market.

Better Customer Engagement:

IA helps improve CX by improving product customization, automating order tracking, and streamlining after-sale support. AI-powered chatbots and self-service portals offer on-the-go assistance, while predictive analytics improve customer satisfaction.

KPIs to Measure Intelligent Automation Impact in Manufacturing

How can Tx Assist with Automation in the Manufacturing Industry?

At Tx, we assist our manufacturing industry clients with next-gen automation solutions that improve efficiency, agility, and precision. Our team of AI-driven test automation helps businesses improve the quality of their software solutions while reducing costs and downtime. Partnering with Tx would give you access to the following perks:

AI-driven Test Automation:

We leverage AI-powered automation accelerators (Tx-Automate) for quick and frequent quality checks, reducing defect rates and time to market.

Supply Chain Management:

Our software testing solutions help streamline supply chain management with AI-based demand forecasting and automated inventory control, ensuring optimized resource allocation.

Scalable Automation Strategies:

We design scalable automation solutions tailored to industry-specific needs, whether RPA, AI-powered decision-making, or autonomous systems.

RFID Solutions:

We leverage RFID solutions in software testing for tasks like asset tracking and inventory management, ensuring the integrity of test data and offering real-time automation and visibility.

Summary

Intelligent automation in the manufacturing industry enables efficiency enhancement, cost reduction, and product quality improvement. It helps manufacturers handle extensive data and streamline ordering, workflow scheduling, and procurement. By pairing with innovative technologies like AI, IoT, and RPA, businesses can speed up their transformation efforts and free their workforce from redundant tasks. Partnering with Tx would accelerate your software QA process and ensure you deliver better customer engagement and increase profits. Contact us now to know how the Tx manufacturing industry and automation experts can help.

The post Modern Manufacturing’s Silent Revolution: Intelligent Automation first appeared on TestingXperts.

The industry is shifting from quality assurance to quality engineering, and automation is one of the core enablers of this transformation. Automation is no longer about executing repetitive test cases. It’s an approach to integrating quality in every step of SDLC. In this context, shift-left automation speeds up the development process by automating the early identification of issues and quality improvement. This also ensures the faster delivery of products, reduces testing costs, and enables businesses to detect and resolve defects proactively. But what makes this approach “smart”?

This blog will help you uncover this question and understand the role of intelligent testing in driving innovation.

Shift-Left Automation: An Overview

Shift-left automation involves integrating automated testing at the beginning of the software development stage. This allows teams to detect and address issues proactively and reduce the chances of defects reaching production. In modern DevOps and Agile practices, shift-left automation ensures continuous testing and integrated feedback loops. Smart shift-left automation leverages AI-driven analytics and intelligent test execution to optimize software quality and delivery timelines.

As software applications become more complex daily, traditional end-stage testing will not be enough. Shift-left automation is crucial because of the following reasons:

• Fixing defects early in SDLC stages is much cheaper than addressing them in the production environment.

• To enhance software quality, continuous testing helps detect performance, security, and functionality issues.

• Automated quality checks seamlessly integrate into CI/CD pipelines for faster releases.

• Ensures rapid iterations without compromising quality.

How Shift-Left Automation Enhances Software Quality?

Early Bug Detection and Mitigation:

There’s a saying that “Prevention is Better than Cure.” One primary benefit of shift-left automation is that teams can catch defects before they become costlier to fix later. Teams can start with the QA process at the requirement and development stages. They can identify code errors, security gaps, and performance bottlenecks early and reduce the chances of bugs in the later stages. Integrating test automation from the beginning is the right way to prevent issues rather than just detecting them.

Seamless CI/CD Integration for Continuous Quality:

Shift-left automation embeds quality checks within the development pipeline to enable CI/CD integration. QA teams can run automated test cases with every code commit and provide instant feedback to the developers. By doing so, they can fix issues as soon as they get detected, leading to faster software release and better stability.

Better Test Coverage and Efficiency:

By syncing testing at every SDLC stage, businesses can improve test coverage across functional, UI, security, and performance aspects. Automated test suites ensure new code changes do not introduce new failures. By eliminating last-minute testing, businesses can make testing a continuous process. This will also reduce duplication and allow teams to focus on high-priority areas rather than focusing on unnecessary test cases.

Smart Testing in Shift-Left Automation

AI/ML for Predictive Defect Analysis:

AL and ML models assist with predictive analytics to identify potential defects before they occur. These models analyze past test records, patterns, and system behavior to identify areas vulnerable to failure. Teams can focus their testing activities on high-risk modules and reduce the chances of defect slippage. AI/ML-powered predictive analytics also optimize test cases and improve QA efficiency.

Risk-based and Model-based Testing for Smarter Decision-Making:

Not all test cases are a priority. Risk-based testing prefers activities based on the potential impact of failures to ensure critical functionalities are thoroughly validated. On the other hand, model-based testing utilizes visual representations of system behavior to generate optimized test scenarios.

Self-Healing Test Automation:

Frequent software updates often break automated test scripts, requiring constant maintenance. AI-enabled self-healing test automation detects application element changes and dynamically updates test scripts. This minimizes manual supervision, ensuring tests remain stable across different builds.

How Tx Can Assist with Shift-Left Automation?

At Tx, we assist our clients with implementing the shift-left approach in their agile testing methodology to speed up the development process. Our expertise in test automation, AI tools and technologies, and CI/CD integration allows us to embed QA immediately. We help our clients accelerate software delivery while keeping the risk under check. Our approach to shift-left automation includes:

• Early-stage test automation implementation

• AI-powered Predictive Testing

• Self-healing Automation

• CI/CD Integration

• Risk-based & Model-based testing

By partnering with Tx, you can achieve higher software quality, faster time-to-market, and reduced QA costs. We will help you deliver robust and reliable applications.

Summary

Shift-left automation integrates testing early in software development, enabling proactive defect detection and improving software quality. Unlike traditional testing, which occurs late in development, shift-left automation leverages AI-driven analytics, self-healing automation, and risk-based testing for more intelligent decision-making. It enhances test efficiency, ensures seamless CI/CD integration, and reduces testing costs. By embedding continuous testing, businesses can deliver high-quality software without last-minute delays. Tx empowers organizations with early-stage automation, predictive testing, and CI/CD integration, ensuring faster releases with minimized risks. To know how Tx can help, contact our experts now.

The post Shift-Left Automation: Enhancing Software Quality with Smart Testing first appeared on TestingXperts.

1. Role of AI in Supply Chain
2. 10 Use Cases of Generative AI in Supply Chain
3. Opportunities and Challenges with GenAI Integration
4. How can Tx help with Supply Chain Optimization?
5. Summary

Among all industries, supply chain management might have been the slowest to adopt artificial intelligence. But in recent years, the fragility of global supply chains has caught the public eye, and it has become a necessity for the supply chain industry to dive into the sea of AI. In every country, large logistics networks are vital for manufacturers. However, they face severe challenges due to various factors, such as transportation delays, increasing network complexities, and labor stoppages.

Business leaders steadily integrate cutting-edge technology like AI into supply chain management to untangle these problematic knots. They aim to gain a competitive advantage in their industry and upscale their CX delivery level. Within the AI domain, one solution could help them upscale their key supply chain processes: “Generative AI (GenAI).”

GenAI is trending these days and gaining popularity across various business areas, such as sales, marketing, and development. It analyzes previous data to help businesses improve supply chain resiliency and operational efficiency.

Role of AI in Supply Chain

There are various activities that businesses manage and optimize in the supply chain with the help of AI, such as:

•Inventory Levels

•Product Quality

•Fuel-efficient Delivery Routes

AI applications include simulating human intelligence, performing complex tasks, managing security, etc. Its subfield machine learning (ML) analyzes and learns by consuming huge data sets, enabling AI systems to outrun traditional software functionalities. This allows businesses to perform the following tasks:

• Decrypting information from videos.

• Analyzing and interpreting text and speech.

• Predicting future market trends and customer behavior.

• Assisting in decision-making in complex situations.

• Structuring insights from large data sets.

This makes it necessary for businesses to utilize tech-centered solutions like AI to manage and optimize their workflows across the supply chain. For instance, ML algorithms can optimize supply chain systems by discovering patterns and connections between datasets that go unnoticed by humans or traditional systems. This helps businesses to accurately predict customer demands, leading to efficient and cost-effective inventory management. AI in supply chain management can analyze weather and traffic conditions to suggest faster shipping routes, reducing the risk of delays and thus improving the delivery period. It also monitors workspaces to identify poor quality controls and safety violations.

GenAI, a specific subset of AI, is trained using large datasets. When a user inputs new data, the generative AI app uses new and previous data to generate new content. Businesses can train GenAI models using supply chain data, including logistics, inventory details, and demand. GenAI can analyze organizational data to improve supply chain resilience and management.

8 Use Cases of Generative AI in Supply Chain

Before learning about GenAI uses in the supply chain, businesses must ensure that this technology aligns with their objectives and delivers benefits accordingly. Now, let’s take a quick look at some use cases of generative AI in supply chain management:

Inventory Management

GenAI models can assist in optimizing inventory management. Businesses can train these models using data about stock levels, warehouse capacity, and manufacturing date/time. It will use this information to optimize inventory processes, including recommendations about restocking or reducing stock. This would allow businesses to save expenses on excess storage.

Logistics

Logistics disruptions are a common problem businesses face in supply chain management. Traffic accidents, bad weather conditions, and labor shortages are some of the events making it difficult to maintain shipping schedules. Training GenAI models using past weather patterns, traffic maps, fuel prices, and labor stats can help businesses identify optimal travel routes. It would also suggest alternate routes if there’s an upcoming disruption. This will allow businesses to keep shipments on schedule and improve CX and service.

Analytics

GenAI can run simulations and multiple scenarios to assess risks and draft a report. It can also assist in analyzing and pinpointing the key problematic areas in supply chain processes and recommending the appropriate measures to resolve them.

Demand Forecasting

Supply chain operations are unpredictable. They depend on consumer buying behavior, demand for the product or service, and market prices. Enterprises can train their GenAI models on past sales data, customer behavior, market trends, and other elements to predict future supply-and-demand scenarios and optimize their demand forecasting accuracy. This would allow them to mitigate supply chain disruption and prevent stocking issues.

Operations

Implementing new technologies and fluctuating user demands can cause operational challenges. GenAI can assist businesses in tackling those challenges and improving supply chain operational efficiency. It can assess performance and manufacturing speed, find areas for improvement, and suggest ways to optimize them.

Fraud Detection

One of the primary benefits of GenAI models is that they can be trained and fine-tuned to predict the possibilities of fraud occurrences. GenAI can identify complex patterns by analyzing financial data and can aid in detecting fraudulent activities. Businesses can use GANs (generative adversarial networks) to create fraudulent transactions using generator networks and a discriminator to identify them. This whole system helps improve fraud detection capabilities and will aid in strengthening security protocols in supply chain management.

Supplier Relationship Management

GenAI can assist in identifying reliable partners by analyzing markets and suppliers. It helps establish stronger relationships with suppliers, ensuring a high-quality and consistent supply of services/products. It would help businesses negotiate better, secure favorable contracts, and access competitive pricing. This can reduce procurement costs and optimize supply chain efficiency.

Personalization and Customization

Industries like electronics and fashion require high customization. GenAI can help create personalized and customized products according to user preferences while adhering to production and supply constraints.

Opportunities and Challenges with GenAI Implementation

• GenAI offers countless applications to optimize supply chain operations, which include.

• Customer services automation.

• Document creation synthesis and automation.

• Demand forecasting.

• Predictive maintenance.

It can identify patterns from existing data and insights to help businesses manage their supply chain operations. Despite its potential benefits and opportunities, there are some challenges in supply chain management, including data privacy, security, and access controls for AI tools within the business IT ecosystem. These challenges urge enterprises to develop GenAI solutions in-house or by partnering with vendors, and they miss opportunities and toolkits offered by open models.

The supply chain industry is now partially ready to adapt and integrate GenAI into its operations. However, it will gain momentum later with sufficient support and quality leadership. According to Gartner, 80% of the global supply chain market is still not incorporated into digital business models. This gap will continue to affect supply chain performance goals until sufficient tech investments are synced with decision models.

AI and ML are among the top 10 trends in supply chain management. However, the delay in adopting GenAI could be due to the complexities of supply chains and the need for business-specific model training.

How can Tx help with Supply Chain Optimization?

Caution is a primary factor when dealing with tech, especially AI-based. GenAI models are still evolving, so one must have sufficient measures to tackle AI-related concerns. Incorrect data, imperfect outputs, and AI hallucinations are current concerns that can prevent the effective use of AI in the supply chain. Tx is a leading digital engineering and quality assurance provider offering comprehensive GenAI testing services. We can assist you in tacking generative AI in supply chain implementation challenges by offering the following:

• Dedicated AI testing team possessing in-depth knowledge of GenAI algorithms and their applications in the supply chain.

• A comprehensive testing approach tailored to generative AI models to validate their accuracy and reliability.

• Implementing cutting-edge testing tools and frameworks, including our in-house AI-based accelerators (Tx-Automate and Tx-SmarTest) to enhance the effectiveness of generative AI testing.

• Adherence to data privacy and compliance with supply chain industry regulations to securely handle information during testing.

Summary

The supply chain industry, traditionally slow to adopt AI, now increasingly integrates Generative AI to enhance operations. Generative AI in the supply chain industry aids in inventory management, logistics, analytics, demand forecasting, operations, fraud detection, supplier relationship management, and product personalization. It offers benefits like cost reduction, improved efficiency, and better customer experiences. However, challenges like data privacy, security, and the need for business-specific training hinder adoption. Despite these obstacles, the potential for digital transformation in supply chain management is significant. Tx, a digital engineering and QA service provider, offers GenAI testing services to address these challenges and ensure accurate, reliable implementations. To know how Tx can help, contact our experts now.

The post Top 8 Use Cases of Generative AI in the Supply Chain Industry first appeared on TestingXperts.

1. About Guidewire Software, Inc
2. What are the Benefits of Guidewire?
3. Challenges in Testing Guidewire Suite
4. Guidewire Test Automation Tips for Better ROI
5. Conclusion
6. Why Partner with TestingXperts for Guidewire Automation Testing?
   

The insurance industry is undergoing a rapid digital transformation, making it crucial for insurance companies to implement innovative technologies like Guidewire to remain competitive in the modern marketplace. In the fast-paced data and digital engagement areas, the latest release of the Guidewire Insurance Platform enables insurers to mine and deploy deep insight from the large volume of data available to the industry. According to reports, the global guidewire market is expected to hit US$2.6 billion by 2025. But, to successfully implement this platform, companies should invest in automating guidewire testing to prohibit the rising costs and delays of manual testing. It will enable insurance companies to focus on other key areas, such as their services and users, instead of wasting time on manual processes that could waste resources and slow workflow.

About Guidewire Software, Inc

Guidewire Software, Inc. is a US-based software company that offers tailored services to the insurance niche. Their Insurance Suite offers comprehensive applications to support multiple insurance operations such as policy administration, claim management, and billing. The company is also known for recurring revenue as it sells term licenses with software-as-a-service on a subscription basis. Their most popular products are BillingCenter, PolicyCenter, and ClaimCenter, and offer core back-end software services for property and casualty insurance carriers all across the world.

The Guidewire Insurance Platform is based on three elements: data management and analytics, transaction processing, and digital engagement. It also helps make the best business decisions based on data insights and supports digital sales, services, and claim experience for several stakeholders. Some well-known insurance carriers that use Guidewire are the Insurance Corporation of British Columbia, United Automobile Insurance Company (UAIC), Canadian Automobile Association Insurance Company (Ontario), and The Cincinnati Insurance Companies.

What are the Benefits of Guidewire?

Guidewire Suite is a software platform that focuses on upscaling insurance operations to deliver seamless services to an active global customer community. It is known for streamlining operations, managing insurance process complexities, and improving customer experience. Following are some key benefits of using Guidewire

• It allows insurance companies to implement solutions according to their requirements and upscale them as their business grows. This type of flexibility allows companies to easily adapt to changing customer demands and market structure.

• The software offers greater accessibility and flexibility with its cloud-based solution, enabling insurance companies to implement cloud computing into their existing business infrastructure. This, in turn, results in improved data security and reduced IT infrastructure costs.

• It offers advanced data management and analytics tools to analyze vast amounts of data, allowing insurers to gain insights into customer behavior, market trends, risk assessments, and operational efficiency, leading to informed decision-making.

• Its set of tools allows insurers to provide personalized services to the clients, improve response time, and efficiently manage claim processing, leading to overall customer satisfaction.

• Guidewire Suite can easily integrate with existing insurance operations such as claim management, billing, policy administration, and underwriting. This reduces error risk, minimizes manual tasks, and improves operational efficiency.

• It allows insurance companies to comply with changing regulatory requirements by adapting to new regulations, ensuring that the businesses remain compliant. The software also supports risk management through enhanced underwriting processes and risk assessment.

Challenges in Testing Guidewire Suite

Due to the complex and customizable nature of Guidewire Insurance Suite, testing it comes with a set of challenges. While the implementation process can be handled by IT experts, the testing and integration of the Insurance Suite within the business operations are very critical. Guidewire testing remains a major challenge, as manual testing is time-consuming and tedious task. Let us look into some of the challenges encountered by QA teams during Guidewire testing

• As Guidewire offers customizable solutions, testing each customization often involves complex business rules and configurations, which is time-consuming and challenging.

• Data migration is a critical process for companies transitioning to Guidewire software. Testing migrated data accuracy, and integrity becomes complex as it requires various validating techniques.

• The Guidewire Suite needs to be integrated with external systems, thus, ensuring seamless integration and data consistency presents a significant testing challenge.

• Testing Guidewire Suite for reliability, performance, and scalability during peak business hours is challenging.

• Insurance business owners should follow strict regulations and compliance, whether regional or global. So, conducting compliance testing for Guidewire implementation based on rules and policies becomes challenging and critical.

• Insurance businesses handle sensitive data, and ensuring security is necessary. Thus, doing vulnerability assessment to ensure data protection, especially for a complex and integrated system such as Guidewire, becomes tedious.

• It requires effective and frequent regression strategies to check the regular patches and updates Guidewire releases to ensure new changes do not negatively affect existing features.

• Testing Guidewire software requires a deep understanding of end-user requirements to ensure a seamless user interface and streamlined workflows for usability.

Guidewire Test Automation Tips for Better ROI

By automating test cases for Guidewire testing, businesses can reduce operational costs by 70% and improve their team’s efficiency. Automation will help improve product quality as the test can run repeatedly without human help and errors. Following are some of the benefits of test automation to make Guidewire testing more feasible such that it can offer better ROI.

Efficient Testing

The two main significant benefits of test automation are efficiency and consistency in test cases. With manual testing being time-consuming, businesses should implement automation to make the process faster, consistent, and cost-saving, leading to an efficient testing process and a higher return on investment.

Better Stability

It offers a stable platform for Guidewire testing requirements by reducing the monotony due to repetitive tasks. Test automation also reduces the error rate by following pre-determined instructions. Automating regressing tests allows testers to analyze whether the existing functionalities are compatible with the latest Guidewire updates.

Simulating Testing Types

Test automation helps in simulating different testing environments. For example, GUI testing is one of the most complex and time-consuming software testing processes. Test automation simulates the user-driven environments to check the real-time issues in the application.

Test Cases Reusability

Automation facilitates the test case reusability feature to help businesses increase their profitability across the product lifecycle. It enables better test management, seamless updation of the test directory, and efficient bug discovery. Test automation also minimizes costs, which is impossible with manual testing, leading to better ROI.

Guidewire Insurance Suite-specific Testing

Guidewire software testing is designed for the insurance industry-specific needs, which include system-integrated testing, functional testing, user acceptance testing, performance testing, test automation, etc. The in-house accelerators Tx-HyperAutomate and Tx-Automate will not only accelerate the test automation process but will also allow seamless integration of open-source tools. These tools significantly offer higher ROI for insurance software testing needs.

Conclusion

Guidewire Insurance Suite is a crucial tool for insurance businesses to facilitate successful digital transformation. For insurance companies seeking to improve customer experience, enhance operational efficiency, and remain competitive in the digital market, Guidewire’s robust features and customization make it an indispensable tool. However, one must implement robust testing steps to integrate Guidewire Suite into the existing business operations successfully. Test automation is necessary to counter the testing challenges that arise in Guidewire Suite implementation. It not only streamlines the testing process but also ensures cost-effectiveness, efficiency, and accuracy.

Why Partner with TestingXperts for Guidewire Automation Testing?

At TestingXperts, we understand the importance of investment that insurance companies make in Guidewire Insurance software products and the ROI they expect from it. Various insurance companies have partnered with TestingXperts for software testing services and Guidewire’s pre-built testing suite. Our QA services cater to key insurance areas such as third-party administration, underwriting modules, risk management, regulatory and compliance, risk management, advanced analytics and business intelligence, and more. You get assured of a 30% faster time-to-market, a 90% reduction in man-hours, 40% cost savings, and higher operational efficiency. We address the following insurance industry challenges with our next-gen QA services:

• Modernize core insurance systems to make them future-proof for Guidewire implementation.

• Help businesses comply with industry rules and regulations.

• Improve IT efficiency with seamless QA services and reduce total cost of ownership.

• Our in-house accelerators Tx-Automate and Tx-HyperAutomate, address the Guidewire automation testing process.

• Ensure seamless integration of third-party vendor systems.

To know more, contact our QA experts now.

The post Does Automating Guidewire Testing Improve ROI for Insurance Companies? first appeared on TestingXperts.

Selecting the right end-to-end (E2E) testing framework is very important in the web application testing domain. It allows businesses to automate test cases and ensure their applications perform as expected throughout the testing cycle. However, with many cross-browser testing frameworks covering the market, selecting the right tool for automated testing requirements becomes harder. Although Selenium, by default, has been the suitable choice for automated testing, Playwright and Cypress have also emerged as good alternatives in this domain. However, to understand which tool is best between Playwright vs Cypress, we will talk deeply about it in this blog. 

Why Selecting the Right Framework Matters? 

Selecting the right framework is crucial as it directly impacts the testing process’s scalability, efficiency, and accuracy. It leads to higher-quality software, enabling faster test execution, improved test coverage, and reduced maintenance costs. This allows QA teams to focus on critical testing scenarios while tools handle the repetitive tasks. Let’s take a quick look at some key reasons for selecting a proper E2E testing framework: 

• A framework will facilitate code reusability and create test components that can be used across multiple test cases. This will save the time and effort of the dev and QA teams during development.  

• Teams can utilize a structured approach to minimize human error and ensure consistency in test execution. This will yield more reliable test results.  

• A well-structured framework facilitates easier updates and modifications to test scripts, mainly when changes occur in the application under test.  

• The proper framework can adapt to growing project complexities, efficiently allowing teams to run larger test suites across multiple environments.  

• Frameworks facilitate better communication and collaboration within the team by providing a standardized approach to test development.  

• Selecting a framework that suits the project’s specific technologies, application architecture, and testing requirements ensures optimal results.  

Playwright vs Cypress: A Detailed Comparison

Playwright is an open-source automation framework developed by Microsoft that supports end to end testing of web applications. This tool allows developers and QA teams to automate web page interactions and ensure reliability and robustness. It offers various features, such as: 

• Cross-browser testing to test against multiple browsers simultaneously, reducing application testing time. 

• Parallel testing to run tests in parallel and further decrease overall test run time. 

• Headless testing to run tests in headless mode and test applications that cannot run in a browser. 

Cypress is also an open-source E2E testing framework developed by the Cypress.io team. It focuses on modern JavaScript frameworks like Angular, React, and Vue. It comes with the Mocha test framework by default. This tool is known for its developer-friendly features and simplicity, making it a popular choice among QA and development teams. It offers various features, such as: 

• Intercepting and inspecting network requests for testing APIs and other functionalities dependent upon the network. 

• Automatic waiting enables elements to load and be visible before users interact with them, which reduces test flakiness. 

• It can automatically take videos and screenshots of the test cases to support debugging and reporting. 

Let’s take a quick look at the detailed comparison between Playwright vs Cypress to understand these frameworks better: 

Playwright vs Cypress Use Case Scenarios: Which is Better? 

For Fast-Paced Agile Teams:

Speed, reliability, stability, and ease of integration are the critical factors for teams that work in an Agile environment. They have to consider all these factors when selecting a testing framework. Playwright supports parallel test execution to reduce overall test run time, making it a strong contender. Its auto-waiting feature ensures stable test case execution and minimizes flaky failures to speed up development cycles. It also supports multiple programming languages, making it easy to adopt. On the other hand, Cypress is known for its easy-to-setup and user-friendly syntax. It is an excellent tool for front-end teams looking for quick adoption. However, the test run is slower in Cypress than in Playwright due to browser execution constraints, a drawback in a fast-paced Agile environment. 

Comprehensive Cross-Browser Testing:

Playwright is the first choice if cross-browser compatibility is a priority. It offers native support for multiple browsers, ensuring consistent performance across each. Additionally, it supports accurate mobile browser testing, making it highly suitable for applications targeting various devices. Cypress, in comparison, is primarily optimized for Chrome-based browsers. Although it provides experimental support for Edge and Firefox, it lacks full-fledged cross-browser capabilities. Furthermore, it does not support accurate mobile browser testing, which can be a limitation for teams requiring extensive compatibility testing. 

Debugging and Test Maintenance:

Effective debugging and maintenance are crucial for long-term test automation success. Cypress leads in debugging with its interactive UI and time-travel feature, allowing testers to replay previous test steps for more straightforward issue resolution. It is preferred for teams focusing on front-end testing where quick UI validation is required. However, Cypress tests can be prone to flakiness due to browser constraints, increasing maintenance overhead. On the other hand, Playwright provides auto-waiting, event tracing, and rich debugging tools, which help reduce test flakiness and improve test stability. Its detailed test reports and seamless CI/CD integration make it a better fit for teams requiring long-term test maintainability and scalability. 

How does Tx help businesses with Cypress and Playwright Automation? 

Tx is a leading DA and QE service provider and has been chosen as a trusted QA partner by Fortune clients. We ensure superior testing outcomes for our global clientele. Our deep expertise in E2E testing services across industries like insurance, retail, BFSI, eCommerce, etc., makes us experts in utilizing the full capabilities of Playwright and Cypress frameworks. Our team of experts has extensive experience using both these frameworks to develop automation scripts that can streamline QA processes and reduce overall test run time.  

Depending upon the business requirements, we recommend that our clients use the fitted E2E test automation framework to ensure rapid and reliable test execution. Our team of experts can develop a customized automation framework using Playwright or Cypress, ensuring it is tailored to your specific needs. We can also create automation scripts that simulate real-world interactions with your application, ensuring it is tested thoroughly. Partnering with Tx ensures that you utilize the full potential of Playwright and Cypress based on your project requirements. 

Summary 

Playwright and Cypress are leading end-to-end testing frameworks for web applications. Playwright excels in cross-browser testing, parallel execution, and scalability, making it ideal for Agile teams. Cypress offers simplified debugging, interactive UI, and seamless front-end testing but lacks full cross-browser support. Playwright is better for comprehensive automation, while Cypress is suited for UI-focused testing. Businesses can maximize efficiency by selecting the right framework based on project needs. Tx provides expert automation services, leveraging both frameworks to ensure reliable test execution. Partnering with Tx enables businesses to optimize their QA strategy and achieve superior software quality. To learn how Tx can assist you, contact our E2E automation experts now. 

The post Playwright Vs Cypress For End-to-End Testing: Which Is Better first appeared on TestingXperts.