1. Quality assurance vs quality control
2. An overview of QA processes
3. Quality assurance and quality control: The similarities
4. What is the Difference Between Quality Assurance and Quality Control
5. Conclusion
6. How can Tx help?

Digital businesses must deliver exceptional customer experiences that ensure user retention, customer loyalty and a relentless revenue stream. While the stakes are high and businesses compete aggressively to achieve digital transformation, quality remains at the focal point.
Quality assurance (QA) vs quality control (QC) are two key aspects of product or service quality management. There is no denying of the fact that some quality assurance and quality control activities may be interrelated, but the two processes differ. When comparing quality assurance vs quality control, both processes share similar objectives but employ different strategies to ensure customers in the UK receive finished products of the highest quality.

Quality assurance vs quality control

What is Quality assurance?

Quality assurance defined as a quality management initiative aiming to provide confidence that an application’s digital quality requirements will be fulfilled. Quality assurance is twofold—internally to the stakeholders and externally to the final users, regulatory authorities and third parties.

An overview of QA processes

QA involves activities and procedures performed during the software development life cycle that help ensure a high-quality application release. QA includes members across departments discussing, planning and executing tests to validate product quality.

A QA process deals with the following aspects:

• Requirement gathering, designing and prototyping

• Programming and code reviews

• Creation of version control

• Continuous integration/continuous delivery (CI/CD)

• Configuration management

• Test planning and execution

• Release management

• Deployment and integration

• Customer acceptance and feedback

What is Quality control?

Quality control is defined as a quality management process aiming to fulfil an application’s quality requirements or service. While quality assurance deals with how processes and procedures are performed, or an application is created, quality control can be termed as the ‘inspection’ aspect of quality management system.

An overview of QC processes

In QC, it is ensured that the developed application meets the organisation’s quality standards. Defects in an application, such as UI bugs, design glitches, accessibility issues or security flaws, can cause irreparable damage to both the end users as well as the product businesses.

A QC process deals with the following aspects:

• Identifying application errors

• Verifying predefined quality requirements are met

• Ensuring conformance with industry compliances and regulations

• Creating and maintaining quality reports

• Determining features where UX could be uplifted

Quality control vs Quality assurance: The similarities

Ensures a high-quality product:

A business can’t simply release a product and hope for the best. QA helps digital organizations plan how they will approach product quality. QC activities helps ensure digital quality businesses that the finished products meet expectations. QC and QA play vital roles in delivering top-quality services and products to customers and securing revenue for the business.

Ensures adherence to policies and procedures:

While QA is more process-oriented than QC, both approaches follow standards set by the organisation for securing high quality. QC might involve some exploratory or UX testing that demands a certain level of creativity on the tester’s part. However, defect detection and remediation must still be recorded and executed systematically.

Mitigates major quality issues:

Defects may be in the form of minor inconveniences or highly escalated disruptions. The earlier such defects are identified, the more cost-effective and easier it is to fix the issue. The risk increases exponentially as defects move closer to the customers’ hands. QC and QA aim to achieve reduced time and cost to market for digital businesses. The former helps to establish processes for early detection, and the latter helps businesses fix as many errors as possible in the application.

Improves the organisation’s product development processes:

Feedback loops are a familiar idea for developers. Organisations focused on processes of QA and QC follow a continuous learning path and eventually become more efficient and effective with each product release. QC and QA enable digital businesses to understand where they may improve in critical areas of planning, automation, bug detection, etc., to enable a great CX.

What is the Difference Between Quality Assurance and Quality Control

Conclusion

Quality control and quality assurance are essential for delivering high-quality applications and services in the UK. While quality assurance focuses on preventing defects and ensuring overall product quality, quality control detects and fixes defects. Both processes are integral to effective quality management.

How can Testingxperts help Quality Assurance Services?

TestingXperts (Tx) is one of the five largest global pure-play software testing services providers. Tx has been chosen as a trusted QA partner by Fortune clients and ensures superior testing outcomes for its global clientele. We have rich expertise in enabling end-to-end testing services for global clients across various industry domains like healthcare, telecom, BFSI, retail & eCommerce, etc.

Our wide range of QA testing services in the United Kingdom includes:

Test Advisory:

TestingXperts’ Test Advisory Services provide specialist advice to help achieve overall product quality, reduced release cycle time, faster time-to-market, and significant reductions in QA costs.

DevOps Consulting Services:

Our DevOps experts will analyse business needs and help achieve a strategic DevOps adoption roadmap. Our wide range of DevOps services like DevOps consulting, DevOps CI/CD implementation, Enterprise DevOps Transformation, IAC consulting and Implementation, and Test Automation in DevOps will help achieve faster and quality releases.

Test Automation:

Our in-house test automation experts have rich expertise in industry-leading test automation tools. Our end-to-end test automation services help businesses reduce regression time and cost.

Functional Testing:

Tx has years of experience providing functional testing services for clients across domains. Our range of functional testing services includes automation, manual, regression, and mobile application testing. We have in-house mobile test labs with numerous mobile devices.

Non-Functional Testing:

Tx provides a wide range of non-functional testing services such as performance and load testing, security testing, usability testing, and accessibility testing. We also have an in-house performance testing accelerator, ‘Tx-PEARS,’ which helps with all non-functional testing requirements, including continuous monitoring of your infrastructure in production and lower environments.

Next-gen Testing:

Our wide range of next-gen testing services includes AI testing, RPA testing using UiPath Test Suite, Digital testing, IoT testing, etc.

The post Quality Assurance vs Quality Control – A Quick Overview first appeared on TestingXperts.

What is Quality Assurance (QA)?

Quality Assurance or QA testing determines whether the software meets the specific quality standards. Software quality assurance involves evaluating the software based on functionality, performance, usability, security, etc. QA testing also involves checking the quality of the process used to develop, test, and release the software. Thus, QA is an integral part of quality management focused on providing confidence internally to business managers and stakeholders and externally to customers, government agencies, regulators, and third parties.

According to Wikipedia, Quality assurance (QA) prevents mistakes and defects in manufactured products and avoids problems when delivering products or services to customers. ISO 9000 defines QA as part of quality management focused on providing confidence that quality requirements will be fulfilled.

Benefits of leveraging QA testing services

Ensures proper procedures are followed:

By leveraging QA testing, businesses can ensure that their software is developed and tested by following proper procedures throughout the software development lifecycle (SDLC).

Ensures bug-free software:

QA testing ensures that software is free from bugs as the product is tested end-to-end by QA testers.

Improves product quality:

QA teams perform testing, process monitoring, audits, and product evaluation to enhance the overall product quality.

Protects brand reputation:

By leveraging QA testing, businesses can improve product quality and achieve bug-free software that ultimately uplifts their reputation and delivers a greater customer experience.

Ensures security:

QA testing ensures that the software is built and tested and is free from any security loopholes, which ultimately improves the security-based features of the software.

Improves customer satisfaction:

QA testing ensures that the software has all the required features, works appropriately, and improves customer satisfaction.

Major difference between QA and QE

QA teams help prevent defects in the software by ensuring it is built and tested by following all the procedures throughout the SDLC. This QA process ensures that software meets all quality standards and is free from bugs. On the other hand, Quality Engineering (QE) assures high standards during software development and is explicitly suitable for agile and DevOps methodologies. Typically, quality engineers play a critical role in quality engineering by creating, implementing, and maintaining systems to ensure effective quality assurance automation processes.

Another significant difference is that the software quality assurance team or the software quality analysts focus on implementing quality in the software during its built stage. They even perform quality tests. Whereas the software quality engineers focus on further improving the QA process. They work only after the software quality analysts complete their work and submit QA requirements.

What role does Quality Assurance play in DevOps?

Today’s businesses are rapidly adopting DevOps processes to automate software deployment and embrace a culture where development and testing are taken up parallelly. The DevOps methodology helps ensure quality checks at every step of the software development and ensures faster releases and faster time-to-market.

Since quality is deeply integrated into DevOps, QA becomes an integral part of the DevOps processes, as stated below:

• In a DevOps environment, the QA teams work collaboratively with development and operations teams to ensure faster releases with continuous integration (CI) and continuous delivery (CD) to get high-quality software.

• Quality is ensured with QA in place throughout the software development, software testing, and software delivery cycle within the DevOps processes.

• By ensuring QA in DevOps, the development and testing teams take full responsibility for the software quality.

• QAops is the combination of QA with IT Operation and Development teams. It helps deliver high-quality software at a faster pace and enables better communication between cross-functional teams in the software development process.

• Thus, QA helps release code consistently and ensures quality is not just limited to removing the bugs but is built throughout the software release cycle with DevOps.

Further, businesses across industries need quality software, and seemingly QA plays a significant role in enabling quality products for all industries.

What is the significance of QA across leading industries in 2025?

Banking, Financial Services, and Insurance (BFSI):

Today, the BFSI industry heavily depends on digital transformation technology and needs high-quality apps to deliver a great CX, which can be achieved by leveraging QA testing. Some of the other factors which establish QA testing needs for the BFSI sector include:

• BFSI apps usually have multi-tier or N-tier architecture with several underlying app layers that need effective QA testing validation.

• BFSI apps should have proper integration with third-party apps and payment gateways, which requires QA testing.

• BFSI industry should ensure no app downtime or server outage situations, which is ensured through effective QA testing.

• It is essential to conduct QA testing of BFSI apps to ensure there are no security loopholes in the app that cyber attackers can exploit.

• Moreover, BFSI apps must comply with open banking standards, regulatory compliances, and web content accessibility standards which are ensured through effective QA testing.

Retail/eCommerce:

The demand for high-performing eCommerce apps is rapidly rising. Even a small bug in an eCommerce app can drastically decrease sales volume or result in visitor/customer loss, revenue dips, and more. Thus, QA testing is essential for eCommerce and retail apps. Some of the other factors include:

• QA testing ensures seamless eCommerce app cart functionality for sudden price changes, discounts, special offers, or when a new product is added to the shopping cart.

• To ensure the correct display of the product page, proper app navigation, and fully functional links, QA testing is essential.

• Also, eCommerce apps must perform seamlessly on all devices, platforms, and OS to attract more customers, which again needs QA testing.

• eCommerce apps must be secure as it contains customer’s sensitive data like their phone no, banking credentials, card details etc. which must be protected by leveraging QA testing

• QA testing ensures eCommerce apps comply with WCAG guidelines to ensure seamless access of apps to all.

Telecom:

The Telecom industry plays a critical role in keeping the world connected, and as the apps are interconnected, they should perform seamlessly, which needs QA testing. Some other reasons why the telecom industry needs QA testing include:

• Telecom apps usually are complex and have several underlying API layers which need effective validation, which can be achieved through QA testing.

• The Telecom industry has to deal with complex tasks like network configuration, network inventory, fault management, and service provisioning, which need proper QA testing.

• By leveraging security testing, telecom businesses can be ensured that their apps are free from vulnerabilities and security flaws.

• Further, QA testing can also help telecom businesses optimize their time-to-market and reduce the number of customer complaints.

Healthcare:

The healthcare sector deals with critical patient information and must ensure that healthcare, pharma, and other apps are free from bugs and security loopholes that need effective QA testing. Some of the other reasons why the healthcare sector needs QA testing include:

• QA testing is required for the functional validation of the healthcare apps

• Healthcare apps need to be accessible to all people, including people with certain disabilities, which can be ensured by leveraging QA accessibility testing.

• The healthcare sector can ensure quick loading speed, effective app navigation, and seamless compatibility of their apps across OS and devices by leveraging QA testing.

• The healthcare industry is data-rich, and there is an essential need to store and organize information properly, which needs big data testing, an integral part of QA testing.

Travel and Logistics:

Logistics is an essential part of the supply chain for every industry and is constantly under pressure to deliver products faster. Due to the rapid technological advancements, the logistics sector needs to adopt disruptive QA solutions to ensure continuous improvement, enhanced communications, better visibility, and faster delivery. Some of the reasons why this industry needs QA testing include:

• With QA testing, the travel and logistics industry can save itself from situations of communication failure that can lead to fatalities, delayed deliveries, inappropriate supplies, and loss of business, client, reputation, and money.

• QA testing also improves the operational efficiency of the logistics industry

• Security vulnerabilities can be identified and removed from the logistics and related software by leveraging security testing.

• The logistics sector heavily depends on technology and is adopting cloud apps rapidly. QA testing ensures proper functioning, security, and data migration from physical storage to cloud apps.

Best QA practices for software testing in 2025

Ensure continuous monitoring:

Ensures continuous monitoring of the SDLC process to ensure that the agreed standards and procedures are followed while the software is built and tested.

Include risk management:

For an effective QA process, testers must include risk management and other related processes to identify, assess, prioritize, monitor, and control the risk.

Check compliance with industry standards:

QA automation testers must check that a product meets all the necessary compliance and industry standards by leveraging compliance testing.

Prioritize bugs based on risk and usage:

It is best to prioritize the bugs before fixing them based on the risk and usage of that particular area of an app.

Ensure testing throughout the SDLC:

Testers need to conduct QA testing throughout the SDLC to remove bugs as early as possible.

Automate regression suite:

QA automation testers must automate the regression suite to ensure automated regression tests whenever changes in the software are introduced. The automated regression suite ensures there are no bugs in the software even after frequent code changes.

Ensure accurate reports documentation:

It is best to document the software testing results in a report as it acts as excellent training material and helps stakeholders make informed decisions.

Shift-Left and Shift-Right Testing

Shift-Left Testing moves testing earlier in the development lifecycle, catching defects early and reducing costs. With AI-powered tools, this approach is becoming increasingly automated. Shift-Right Testing emphasizes continuous testing in production, leveraging real-time feedback to enhance user experiences and ensure system reliability.

Major types of QA testing for businesses to leverage

Functional testing

• Unit testing

• Integration testing

• Regression testing

• Test automation

• User acceptance testing

Non-functional testing

• Security testing

• Performance and load testing

• Usability testing

• Accessibility testing

Next-gen & Specialized testing

• RPA testing

• AI-based testing

• Digital testing

• IoT testing

• Blockchain testing

• API testing

• Cloud testing

• Data migration testing

Top 5 QA Trends to watch in 2025

QAOps to leap in 2025:

Today’s digital businesses are rapidly adopting DevOps methodology to deliver software faster. However, faster delivery alone is not enough to deliver a great CX, and it is essential to ensure quality along with faster delivery. This is where QA in DevOps or QAOps plays its role, and it is expected to take a leap in 2025.

RPA to lead charts in 2025:

Robotic Process Automation (RPA) is an extension of automation, and it can be applied to automate almost anything in a structured form. In the upcoming years, the usage of RPA in software testing is expected to rise more as it will let testers automate large and complex data through an easy-to-use interface. Testers will be able to give commands to already taught robots to perform several tasks easily and can significantly save a lot of time and effort.

According to Globenewswire, the Global Robotic Process (RPA) Automation Market size is expected to reach $7.2 billion by 2025, rising at a CAGR of 32.6% during the forecast period of 2019-2025.

Rise in the usage of AI and ML in software testing:

As businesses have moved towards digital transformation, it has become essential to embed advanced technologies like AI and ML into software testing. AI and ML in software testing can help businesses understand customer behavior, predict market demand and deliver software faster with advanced testing techniques. Due to this reason, the use of AI and ML in software testing is expected to rise more in 2025.

According to Verified Market Research, the Global Artificial Intelligence (AI) software market is forecasted to reach $2,740.46 billion by 2031.

Low code/no-code automation to gain prominence:

The low-code/no-code automation platforms are software programs that require little to no coding knowledge. It offers pre-designed workflow templates and a user-friendly drag-and-drop interface for customizing workflows that people without back-end computer programming knowledge can use easily. Since it provides various benefits to businesses, like faster time to market, scalability, self-service capability, etc., their usage is expected to grow more in 2025.

According to NASSCOM, the global low-code development industry was valued at $13.8 billion in 2021 and is expected to grow at a CAGR of 28.1% during the forecast period 2021-2025

Cyber Security Testing to gain more attention:

Due to the rapidly rising cyber-attacks, cybersecurity has become an essential need for all industries and has also become a boardroom discussion for CXOs and CIOs. Today, businesses have realized the importance of security testing of their applications, networks, and systems to ensure secure transactions and complete protection of their customer data. Thus, cyber security testing has gained a lot of importance, and its prominence is expected to rise more in the coming years.

According to Mordor Intelligence, the Global Cybersecurity Market is expected to register a CAGR of 14.5% from 2025 to 2027.

Leading QA testing tools in 2025

UiPath Test Suite

It is an open-source RPA testing tool that can automate any desktop or web app. This RPA tool offers a complete suite for functional testing under one roof. The UiPath Test Suite contains StudioPro, Orchestrator, Test Management Hub, Test Manager, and Robots. UiPath can be hosted in virtual terminals or cloud environments and works well with .Net, Java, Flash, PDF, Legacy, SAP, etc.

Tricentis TOSCA:

It is an AI-driven, next-gen QA automation tools that allows Agile and DevOps teams to achieve test automation goals rapidly. This tool enables teams to go beyond continuous testing with AI. Tricentis allows automating end-to-end testing of software applications. This tool combines multiple aspects of software testing (test case design, test automation, test data design and generation, and analytics) to test GUIs and APIs from a business perspective.

Selenium:

It is an open-source web automation tool and supports all OS like Windows, MAC, Linux, etc., and web browsers like Chrome, Firefox, Internet Explorer, etc. It supports programming languages such as Java, PHP, C#, Python, Ruby, Perl, etc. It comes with a record and playback feature that helps test web applications. Testers can run various scripts in parallel using Selenium, making the QA automation testing process faster.

Katalon Studio:

It is a codeless QA automation tool used to test API, mobile, web, and desktop applications. This testing tool supports multiple platforms like Windows, macOS, and Linux, and it also supports both SOAP and RESTful for API and services testing. This tool has some great features like recording actions, creating test cases, generating test scripts, executing tests, reporting results, and easily integrating with many other tools throughout the SDLC.

Botium:

It is an open-source QA test automation tool provided by Selenium and is used for testing chatbot applications. It supports chatbot makers in training and quality assurance: Chatbot makers define what the chatbot is supposed to do.

Conclusion

Today’s businesses need high-quality products to deliver a greater CX and to beat the competition. Quality assurance automation is an effective way to ensure the quality of products and services, and it can save cost, time, and effort by preventing defects in the software as QA teams identify them. Businesses should leverage software quality assurance services from a next-gen specialist QA and software testing services provider for superior quality products and deliver a great CX.

As a Leading QA testing services provider, how can TestingXperts help businesses?

TestingXperts (Tx) is one of the 5 largest pure-play software testing services provider globally. Tx has been chosen as a trusted QA partner by Fortune clients and ensures superior testing outcomes for its global clientele. We have rich expertise in enabling end-to-end testing services for global clients across various industry domains like healthcare, telecom, BFSI, retail & eCommerce, etc.

Our wide range of QA testing services includes:

Test Advisory: TestingXperts’ Test Advisory Services provide you with specialist advice to help you achieve overall product quality, reduced release cycle time, faster time-to-market, and significant reductions in QA costs. Our Test advisory services will help you move to the next maturity level in QA.

DevOps Consulting Services: Our DevOps experts will analyze your business needs and help you achieve a strategic DevOps adoption roadmap. Our wide range of DevOps services like DevOps consulting, DevOps CI/CD implementation, Enterprise DevOps Transformation, IAC consulting and Implementation, and Test Automation in DevOps will help you achieve faster and quality releases.

Test Automation: We have in-house test automation experts with rich expertise in industry-leading test automation tools. We can help you reduce regression time and cost with our end-to-end QA automation testing services with our in-house test automation accelerator Tx-Automate.

Functional Testing: Tx has years of experience providing functional testing services for clients across domains. Our wide range of functional testing services includes automation, manual, regression, and mobile application testing. We have in-house mobile test labs with numerous mobile devices.

Non-Functional Testing: Tx provides a wide range of non-functional testing services such as performance and load testing, security testing, usability testing, and accessibility testing. We also have an in-house performance testing accelerator, ‘Tx-PEARS,’ which helps with all non-functional testing requirements, including continuous monitoring of your infrastructure in production as well as in lower environments.

Next-gen Testing: Our wide range of next-gen testing services include AI testing, RPA testing using UiPath Test Suite, Digital testing, IoT testing, etc.

To know how Tx quality assurance specialists can assist you with your software QA, contact us now. We’ll assist you and provide the best solutions that suits your business needs.

The post Why Quality Assurance Is Critical for Businesses Across Industries In 2025? first appeared on TestingXperts.

1. What is QA Automation, and Why is it Essential?
2. Top-5 QA Automation Tools
3. How to build a QA Automation Framework?
4. QA Automation Challenges & Their Solutions
5. Benefits of QA Automation
6. Conclusion
7. Why Partner with TestingXperts for Automation QA?

Quality Assurance (QA) automation is now e­ssential in the fast-paced business e­nvironment. It’s vital for software development teams wanting to be e­fficient and competitive. Nowadays, to keep up with quality standards while spee­ding up product creation, using QA automation methods is a necessity. The vast rise of the workflow automation market, forecaste­d to reach more than $34.4 billion by 2030, shows the value­ of automation.

The high manual testing costs and the growing complexity of software­ underline the importance of QA automation. The traditional way of QA testing is usually slow, prone to mistakes, and struggle­ to meet the quick launch date­s the market reque­sts. This lack of efficiency slows down work and can hurt a business’s profits. By sharpe­ning the QA process, companies can be­come more efficie­nt and cut costs linked to manual tests. Automation makes ide­ntifying and fixing errors faster, so top-quality products get to marke­t sooner. This raises customer satisfaction and gives companies an advantage over their rivals.

What is QA Automation, and Why is it Essential?

QA Automation is an essential factor in today’s software development process. This method helps businesses run fixed se­t of tests on the software made­. The main aim is to check if the software­’s actions, dependability, and spee­d meet the se­t expectations.

Unlike che­cking software manually, QA Automation is all about letting tools take ove­r the testing process. It minimizes people’s involve­ment and is great for repetitive tasks, plus tests that nee­d a repeat, no matter the­ changes in conditions. The tests are made to work on different software­ types, settings, and gadgets. This method works really we­ll in areas where te­sts need to be quick and regular.

The Necessity of QA Automation

Automated Quality Assurance­ is not just about rapid testing. It’s about making the deve­lopment process efficie­nt, accurate, and flexible. With software­ apps becoming intricate and demand for quick deployme­nt rising, automated quality assurance­ ensures the quality and flexibility of software­ products. It’s a key element that goes beyond usual testing, assuring software works right and is high-performing and user-friendly.

• Automated quality assurance spe­eds up testing, saving time and re­ducing repetitive te­sts. This speed is important in Agile de­velopment, providing rapid feedback and spe­eding up the software de­velopment cycle.

• Automated te­sts repeat the same­ tasks, ensuring uniform results. This assure­d consistency is crucial in spotting flaws and mismatches that might be misse­d in manual testing due to human error. Manual te­sting becomes difficult as software proje­cts become larger and more­ complex. Automated Quality Assurance adjusts without much e­ffort with the software, allowing complete­ testing of intricate systems. It e­nsures total coverage, even as new features and changes are incorporated.

• Automation le­ts the software team tackle­ meaningful tasks by eliminating redundant te­sts. It helps the teamwork be­tter by freeing up time­ for tasks that need human touch and ingenuity, like­ exploratory testing and enhancing user e­xperience.

• Quality assurance automation is vital for consistently integrating and de­livering models. It quickly and effe­ctively verifies change­s in code and ensures e­ach integration is up to quality standards. Regular testing he­lps detect and resolve issue­s early, resulting in a more solid and de­pendable final product.

Top-5 QA Automation Tools

QA automation tools help te­ams automate repeat and tricky testing tasks more efficiently and correctly. They work for e­verything from websites to mobile­ platforms. Each tool can be adjusted to match the spe­cial needs of varied te­sting settings. Since these tools fit into development workflows and continuous te­sting, they are important in today’s software­ development practice­s.

Sele­nium

It offers a toolset for browse­r automation, works with many coding types like Java and Python and supports seve­ral browsers like Chrome, Safari, etc. Selenium WebDriver is a one-stop framework solution for creating sophisticated automation scripts.

Cypress

This QA tool is for conte­mporary web applications and works faster and more re­liably by operating in-browser. Cypress fe­atures an exclusive te­st runner that provides command viewing during e­xecution.

Appium

Appium is a free tool for mobile­ app testing automation. It caters to iOS and Android for cross-platform testing. Te­sting native apps with Appium doesn’t nee­d an SDK or app recompilation, making it popular in mobile QA automation.

TestComple­te

Developed by SmartBe­ar Software, this tool automates UI testing for de­sktop, web, and mobile apps. It supports differe­nt scripting languages and has record and playback functions, appealing to both novice­s and experts.

Jenkins

This isn’t a te­sting tool. Still, Jenkins is a critical tool in QA automation be­cause it enables continuous integration and delivery. It automates code­ deployment and testing, making proje­ct changes easier and pre­venting app breaks.

How to build a QA Automation Framework?

Developing a QA Automation Framework is a strategic process that involves careful planning and execution. It’s about creating a structured environment that allows automated testing to be more efficient, maintainable, and scalable. Following are the steps involved in building a QA Automation Framework

Step-1 Define Objectives and Scope

First, ge­t clear on what you want. Choose the te­sts for automation (maybe unit, integration, or UI tests) and se­t specific targets, like faste­r testing or better cove­rage. Also, decide the­ mix of platforms (web, mobile, desktop) and programming language­s you need to support.

Step-2 Choose the Right Tools

The right tools make all the diffe­rence. Your choice should fit your tech, support your chose­n tests, and come with good help re­sources. Selenium, Cypre­ss, and Appium are tools suitable for QA automation tasks.

Step-3 Design the Framework Architecture

Your framework’s archite­cture shapes how you create­ test scripts. It could be modular, data-driven, ke­yword-driven, or hybrid. This stage is about organization, handling data, and preparing for diffe­rent test scenarios.

Step-4 Develop Test Scripts

Now, create­ your test scripts according to your framework design. Aim for scripts that are­ reusable, easy to maintain, and scalable­. Design a standard function library and templates for te­st cases, and ensure scripts are­ clear and straightforward.

Step-5 Implement Reporting and Logging

Setting up sturdy reports and logs is critical for monitoring te­st activities and results. It should offer cle­ar details about test outcomes like­ pass/fail and point out any alarming areas or identified bugs.

Step-6 Integrate with CI/CD Pipeline

Conne­ction to CI/CD pipelines is nece­ssary for ongoing testing. It requires se­tting up the framework to auto-start tests whe­n code changes or during sele­ct deployment pipeline­ points.

Step-7 Review and Update

Consistently che­ck and adjust the framework to confirm it stays helpful and in sync with pre­sent best strategie­s. It involves renewing tools, fine­-tuning scripts, and adapting to shifting software conditions.

QA Automation Challenges & Their Solutions

QA Automation in software de­velopment is vital but comes with challenges. These hurdles could harm the­ automation process if not correctly managed. Grasping and de­aling with these issues is crucial for the­ automation framework to function at its best and achieve­ the expecte­d results in software testing.

Setting Up Challe­nges

Setting up an automation framework can be­ tricky. It is important to pick useful tools and tailor the­m to the specific needs. The­ best way to do this is to research available­ tools. Look at things like how well they work with othe­rs, the support they offer and e­ase of use. Helping the­ team learn through training sessions and workshops can make­ this processes smoother.

Updating Test Scripts

As software­ progresses, test scripts can be­come old. Setting up a regular sche­dule to check over and re­new test scripts is essential. This makes sure­ they match up with any new feature­s or changes.

Merging with CI/CD

Mixing automated te­sts into CI/CD pipelines can have its issue­s. The key is making sure te­sts are reliable and quick for ongoing de­ployment. Pick automation tools that blend easily with CI/CD tools. Using best te­st management practices can streamline the process.

Dealing with Wrong Re­sults

Sometimes, automated te­sts can give us wrong answers, leading us down the­ wrong path. Checking test cases re­gularly and using better validation methods can make­ these inaccurate results happe­n less.

Test Data Manageme­nt

Managing and taking care of test data for differe­nt test situations can be a handful. Methods like­ data pooling, where test data is looke­d after centrally, and creating fake­ data, can make managing test data simple and faster.

Growth Problems

Whe­n the project enlarge­s, expanding the automation framework can be­ tricky. Creating the framework with the­ idea of growth from the get-go is vital. This involve­s using flexible design rule­s and making sure scripts and processes can be­ used again.

Benefits of QA Automation

Incorporating automated te­sting into the software development proce­ss helps companies run smoother, be­ more precise, and quicker time-to-marke­t. QA Automation plays a crucial role in software­ development by making the testing process faste­r and easier. Plus, it makes the­ software product much better in quality.

• Using automated te­sting shrinks the tons of time we’d usually need for extensive tests. How? Automated te­sts are speedy and can run concurrently. That means QA engineers can do more­ tests in less time! It is re­ally helpful in repetitive and re­gression testing, where­ testers must do the same­ tests a bunch of times.

• Automated te­sts run the same steps e­very time, taking out the unpre­dictability and mistakes that can happen with manual tests. This ste­ady approach guarantees that whether a test works or not, giving dependable and repeatable outcomes.

• Automated tools offe­r broader test coverage­ compared to human-done tests. The­se tools can handle lots of hard test case­s each run, reaching leve­ls that are hard with people-run te­sts. They can check for memory le­aks, speed issues and more­ core features.

• Testers can run automated te­sts when new code comes in. If something goes wrong, the team will know right away. This leads to error or bug detection earlier in the development cycle. Early bug detections are­ cheaper and simpler to fix if found be­fore the cycle’s e­nd.

• Automated tests are a must-have for Agile and De­vOps settings. As they rely on fast ite­rations and ongoing deployment, automation allows for spee­dy and constant feedback cycles. This make­s sure each rele­ase is as error-free­ as it can be.

Conclusion

QA Automation holds a big role­ in modern software making. It helps the­ testing process be fast, re­liable, and efficient. The gains from it, like better spe­ed and precision, reduce­d expenses, and more­ extensive te­sting, can’t be denied. As firms handle­ the challenges that come with QA Automation, like proble­ms in setup and operation, the sugge­sted solutions provide a guide for smooth mix and e­nhancement. Using QA Automation, backed by a cle­ar plan and the right tools, is not just a trend but a nee­d in today’s software industry. It quickly allows team me­mbers to produce top-quality software products, leading to staying ahe­ad and satisfying users in a fast-changing digital world.

Why Partner with TestingXperts for Automated QA Testing?

Our QA professionals are deeply committed to excellence, leveraging the latest technologies and best practices in QA automation. We prioritize understanding your business needs, ensuring our approach aligns perfectly with your objectives. Our robust frameworks and methodologies are designed for scalability and efficiency, guaranteeing a streamlined process and high-quality results.

• Our in-house accelerator, Tx-Automate, enhances the efficiency and effectiveness of the testing process, enabling quicker deployment and higher-quality results. Tx-Automate streamlines the testing workflows, reduces manual efforts, and accelerates the overall testing cycle, providing clients with a cutting-edge solution that significantly boosts productivity and quality in software testing.

• Understanding the importance of budget, we provide cost-effective solutions without compromising quality. This approach ensures a high ROI, making your investment in QA automation smart and sustainable.

• Our experts utilize proven methodologies that have been refined over years of practice. This means clients benefit from reliable and efficient testing processes that have been tested in diverse scenarios.

• We employ the latest technologies and tools in QA automation, ensuring that clients receive state-of-the-art testing services. This commitment to technological advancement means your projects are always ahead of the curve.

• Recognizing the need for speed in today’s fast-paced business environment, we focus on delivering rapid results without compromising accuracy or thoroughness.

To know more, contact our QA automation testing experts now.

The post A Step-by-Step Guide to QA Automation Strategy first appeared on TestingXperts.

Table of Contents

1. Quality Assurance (QA) vs Quality Engineering (QE)
2. Making a Move from Quality Assurance to Quality Engineering
3. The Limitations of Traditional Quality Assurance Practices
4. Why is this Change Important?
5. Conclusion
6. How can TestingXperts help you with Quality Engineering Services?

As per a survey, organizations with a strong focus on Quality Engineering experience up to a 40% reduction in post-release defects compared to those relying solely on Quality Assurance practices.

Another survey of software development teams depicts, “68% of project failures were attributed to a lack of clarity and understanding regarding the distinction between Quality Engineering and Quality Assurance.”

These numbers clearly underscore the impact of quality engineering and how important it is to know the difference to understand project objectives better, run efficient processes, and ultimately aim for product quality.

Quality Assurance (QA) vs Quality Engineering (QE)

“While ‘Quality Assurance’ focuses on ensuring the quality of the final product, ‘Quality Engineering’ takes a proactive role in shaping the development of high-quality products and efficient processes. In simple words, QA primarily identifies defects in the software that has already been developed, while QE represents a technical evolution, shifting from predominantly manual processes to predominantly automated ones. Moreover, QE adheres to a ‘shift-left’ methodology and starts test automation early in the software development life cycle to catch and address issues in the beginning.”

Here’s the major difference between Quality Assurance and Quality Engineering:

Both Quality Assurance and Quality Engineering play crucial roles in delivering high-quality software, but they differ in their approach and scope. QE is a more proactive and integrated approach that aims to prevent defects, while QA focuses on post-development validation and compliance.

Making a Move from Quality Assurance to Quality Engineering

Moving from QA to QE is a manageable shift that demands a shift in mindset and processes rather than substantial financial resources. The encouraging aspect is that you don’t need the financial muscle of a tech giant to embrace robust Quality Engineering practices. This transformation can be accomplished by introducing adjustments to your current workflows, including:

QA engineering team transformation

Companies have the capability to retrain their current workforce to effectively navigate the shift from QA to QE. This entails a transformation of the organizational culture and the team’s perspective, along with the enhancement of skills and tools. Given that the existing QA engineers already possess a solid grasp of the product, their transition to QE is likely to be more seamless.

Shift-Left Approach

The ‘shift-left’ methodology incorporates Quality Assurance (QA) right from the inception of the development life cycle, enabling active involvement in software design sessions and the formulation of testing strategies. In a similar vein, Quality Engineering (QE) also engages from the early stages of the development life cycle. However, its primary emphasis lies in crafting a comprehensive test strategy geared towards achieving higher levels of test automation.

DevOps Implementation

Continuous Integration (CI) and Continuous Deployment (CD) pipelines offer substantial time and effort savings by replacing manual and error-prone deployment tasks. These pipelines complement the ‘shift-left’ testing approach, promoting quality right from the initiation of the software development lifecycle. Furthermore, it’s possible to include automated tests that are scheduled to be executed after each deployment.

The transition from QA to QE is a pivotal step for organizations, empowering them to facilitate digital transformation and ensure a consistent, desired customer experience. Quality Engineering (QE) plays a crucial role in creating versatile platforms that integrate a wide range of tools, utilities, and advanced technologies like AI/ML with lifecycle automation encompassing business processes, functional UI, API, test data, and environments. Additionally, this transition provides superior and highly efficient coverage across both code and functionality, leading to a continuous enhancement of product quality.

Automation Testing with AI/ML capabilities

Equipping testers with automation testing expertise enables the utilization of intelligent test automation, resulting in expanded coverage and enhanced quality. The creation of a sturdy automation framework facilitates a seamless transition from traditional QA to more comprehensive Quality Engineering (QE) practices.

The Limitations of Traditional Quality Assurance Practices

Recent shifts in software delivery have been significantly influenced by alterations in software assurance approaches. The surge of agile and iterative development methodologies is chiefly responsible for these transformations. This shift in paradigm has led to the adoption of more frequent, smaller software releases, departing from the lengthy timelines of the past. It encourages teams to embrace simplified approaches and nurtures a culture of adaptability.

The role of quality analysis has undergone significant evolution due to these dynamic methodologies. To align with sprint cycles, comprehensive system testing is now performed more frequently, typically every two or three weeks, in response to the evolving demands of quality analysis. This represents a departure from the previous, slower paradigm where testing was less frequent and more sporadic in nature.

Transitioning from the antiquated paradigm to these contemporary approaches poses certain challenges.

Continuous Demand for Maintaining Good Quality:

It is essential due to the shift towards agile and iterative development, which necessitates the integration of quality into every phase of the development lifecycle, as opposed to relegating it solely to the final stages.

Focus on Automation:

In this evolving context, automation plays a vital role, and there is a proactive pursuit of testing automation solutions to ensure that testing procedures are both effective and thorough.

Enhanced Functions for Quality assurance companies:

Organizations are gaining increased significance and prominence in response to the changing landscape, as they play a crucial role in ensuring that the ongoing adoption of agile and iterative methodologies consistently yields high-quality software products.

Why is this Change Important?

The shift from Quality Assurance (QA) to Quality Engineering (QE) is primarily motivated by the evolution of service-oriented architectural models. In this contemporary mindset, development teams often conceptualize their business implementations as sets of services that collaborate while maintaining individual functionality, guided by rigorous contractual regulations. This transformation has given rise to a range of alterations and factors to be taken into account.

Challenges for QA Engineers:

Conventional QA engineers have faced notable hurdles, including the requirement to generate code prior to its actual existence, engaging in post-design stages to pinpoint critical tests and essential functionalities, and embracing frameworks that replicate dependencies. These adaptations are intended to streamline the workflow and minimize dependence on both upstream and downstream factors.

Shift in Testing Phases:

Unit testing has been moved to an earlier stage in the development process, and integration testing now demands the participation of QA teams at a much earlier point compared to previous practices. This reconfiguration of the testing stages guarantees a more comprehensive and tightly integrated approach to ensuring quality.

Holistic Software Quality:

The shift towards Quality Engineering (QE) represents a comprehensive perspective on software quality, in which quality is not solely the result of testing but is intrinsically woven into the entire development process. This transformation is rooted in the concept that quality engineering commences with the initial architectural choices and extends through development, risk mitigation, early issue identification, and ongoing enhancements.

Conclusion

The choice between Quality Assurance (QA) and Quality Engineering (QE) is a critical consideration for any business. While QA has traditionally focused on testing at the later stages of development, QE embraces a holistic approach, embedding quality throughout the entire software development lifecycle.

In today’s dynamic and competitive landscape, businesses must prioritize not only the end product but also the processes that lead to it. QE, with its emphasis on proactive risk management, early issue detection, and continuous improvement, offers a strategic advantage. Ultimately, the decision between QA and QE hinges on the extent to which a business is willing to invest in engineering quality and ensuring that it is not just a goal, but an integral part of the software development journey. The choice between QA and QE is a critical one that can significantly impact a business’s ability to deliver high-quality software in a rapidly evolving digital world.

How can TestingXperts help you with Quality Engineering Services?

With a proven history of reducing the number of post-production defects by up to 50% and cutting testing costs by 30%, TestingXperts delivers tangible business value by enhancing software quality, reducing rework, and increasing the efficiency of your development processes. By harnessing advanced test automation techniques, our experts help increase test coverage and significantly reduce testing cycle times, thereby saving both time and resources. TestingXperts expertise in continuous testing ensures that quality is upheld throughout the development process, leading to faster time-to-market and improved customer satisfaction.

The post Quality Assurance (QA) vs. Quality Engineering (QE) – What your Business Needs first appeared on TestingXperts.

Over the last decade, the advancements in the software development industry to meet all business needs for enterprises have raised the demand for fast, efficient, robust, and customizable testing processes. In the current digital age, ensuring software products’ safety, reliability, adaptability, and better customer experience is crucial as they are a core aspect of our everyday lives. Traditional QA methods like manual testing and automated test scripting have become outdated and cannot keep up with the advanced QA needed to make the software defect-free. The running cost of these activities is also high. But there’s no need to worry. With AI and ML solutions, the software QA process has made a 360-degree turn, introducing a new method known as Autonomous Testing.  

What is Autonomous Testing?

Autonomous testing (AI Blueprinting) involves using tools and frameworks to automate software testing while minimizing human involvement. Unlike traditional automated testing, it leverages AI and ML techniques to facilitate intelligent decision-making in the quality assurance process. Autonomous testing uses automated tools, scripts, and code to replicate input data and user actions and deliver expected results. To understand it better, let’s take a look at 6 stages of the journey to move from manual to autonomous testing: 

Stage 1: Manual Testing: All QA actions and decisions are made by testers manually. 

Stage 2: Assisted Automation: Testers leverage automation tools for test creation and execution, while managing and maintaining test scripts manually. 

Stage 3: Partial Automation: Test automation tools control various aspects of test creation, execution, and maintenance, yet the majority of testing decisions are still made by testers. 

Stage 4: Automation Acceleration: Test automation tools control test creation, execution, and maintenance to accelerate automation, and generate a list of decision alternatives, chooses one for action, and will proceed only after the approval of testers. 

Stage 5: Intelligent Automated Testing: The automation tools generate QA decision alternatives, evaluate and select the optimal one, and, based on that, perform testing actions. Tester involvement is still an option if needed. 

Stage 6: Autonomous Testing: Autonomous tools handle test creation, maintenance, and execution, learning from failed tests and adapting to changing conditions.  

QA Experts at Tx use a variety of testing frameworks (including in-house accelerators like Tx-Automate, Tx-HyperAutomate, etc.), and tools to automate various software testing aspects, including regression testing, integration testing, compatibility testing, performance testing, security testing, load testing, functional testing, etc. The goal is to completely transform software testing by enhancing efficiency and allowing test engineers to focus on priority activities. 

Is Autonomous Testing better than Traditional QA?

To understand why autonomous testing is better than traditional QA (manual and automation testing), let’s look at the following table: 

6 Key Components of Autonomous Testing

Businesses can utilize AI and ML for autonomous testing in many ways. To do so, they must carefully understand these technologies’ capabilities and integrate them into testing lifecycles. Following are the six primary components of AI-enabled autonomous testing that relate to software testing life cycle stages: 

Requirement analysis and change impact analysis:

AI solutions analyze complex requirements to identify and report potential ambiguities in the software system. By conducting real-time analytics and analyzing previous traffic data, AI can suggest optimal testing strategies. This helps prioritize the high-risk areas for testing.  

Faster test artifact development:

AI helps automatically generate manual test cases by analyzing the requirements, app usage data, and specifications. For instance, businesses can develop test cases using Gen AI tools and prompt engineering, which can help develop test scripts in desirable programming code. By giving a simple command to AI, testers can make test data generation much easier as it delivers comprehensive datasets, which they can export in either XML or CSV format for data-driven testing. 

Test Case Management:

AI categorizes test cases based on risk, bug type, time to fix, severity, impact areas, etc., for easier management. QA teams can then prioritize test cases that need immediate remediation. AI supports managing and anonymizing test data to ensure businesses comply with data privacy regulations.  

Test Scripts Execution:

AI assists businesses by executing test scripts automatically via a test execution engine or a CI/CD pipeline. The process involves simulating user actions and continuously validating applications as updates are done simultaneously. 

Test Result Analysis:

The QA teams leverage the testing framework to compare and analyze actual and expected results. AI helps spot failure patterns and offers recommendations to improve the testing process. It also helps gain insight into application behavior by analyzing results for the past 30/60/90 days.  

Debugging:

AI tools perform root cause analysis for identified defects and provide fixes that QA teams can implement. They also identify the areas where the issue is happening and automatically report results to the development team via notifications or dashboards. 

Impact of Autonomous Testing on Software QA Landscape

Autonomous testing can surely supercharge the software QA process. Testers must learn and consider AI/ML a powerful tool to enhance productivity and become professionals in commanding AI and ML solutions. It will deliver several benefits to the software testing industry, such as: 

• Faster test creation and design with ML-assisted optimization. 

• Better test coverage through edge case scenarios and adhoc scenarios 

• Self-learning capabilities as system can learn by analyzing previous test runs and adjust and improve over time. 

• Self-healing functionality can reduce the testers involvement in maintaining and managing test scripts. 

• Quick adaptation to software changes while reducing manual maintenance and updates requirements. 

• Speed-up testing process by running tests autonomously and continuously, enabling testers to focus on other value-adding activities. 

• Early error detection as AI can identify patterns and anomalies missed by traditional or manual test scripts, improving test accuracy. 

Although autonomous testing can significantly improve the software QA lifecycle, it’s crucial to approach it cautiously to ensure its effectiveness and reliability. Here are some of the considerations: 

• Ensure the testing framework covers different scenarios, including negative testing, edge cases and is unbiased. It should also be easy to maintain and adapt to future updates. 

• Always use high-quality test data and implement robust security controls to protect sensitive data used during testing. Implement clear data governance policies to ensure data integrity and compliance. 

• Monitor the autonomous test performance regularly to optimize test execution time and resource utilization continuously. The system should be able to learn from historical test results and adapt to application changes. 

• Always have human supervision to make critical decisions and address complex issues in case of unexpected failures. Regularly review test results to identify improvement areas and ensure accuracy. 

• Ensure the fairness of the AI algorithms used for autonomous testing and mitigate any bias that might occur. This will ensure transparency in the decision-making process.  

Explore Autonomous Testing with TestingXperts

Today’s rapidly evolving business processes require a solution that is easily scalable, fast, and can evolve. Autonomous software testing does all of that for your business. Unlock the future of quality assurance by partnering with TestingXperts (Tx) for autonomous software testing. By leveraging AI/ML-driven practices, we make testing the fastest process in the DevOps chain. We ensure you are updated with progress, optimize CX, and grow your business. Our approach includes: 

• AI Script Generation: Streamline and accelerate QA process with AI-enabled exploration of software/application functionality and possible user flows. 

• Test Scripts Designing: Design resilient and self-healing test scripts for API, web, and mobile apps via low code/no code interface. 

• Test Execution: Target app functionality by executing test designs and delivering a complete view of feature quality level. 

• Test Results: Deliver relevant test results to make it easy for every stakeholder to see and understand the outcomes showcased in our accelerator dashboard. 

By combining human creativity and AI capabilities, we at Tx offer our clients cutting-edge solutions to optimize test coverage, improve accuracy, and automate the entire QA lifecycle. With our advanced AI-based in-house tools (Tx-Automate, Tx-HyperAutomate, etc.) and expertise, you can release flawless applications with the highest quality and deliver excellent customer satisfaction. Tx can help you reduce regression test cost by up to 90%, improve test coverage by 70%, reduce QA TCO by up to 55%, and accelerate the time-to-market rate by up to 80%. 

Summary

In short, autonomous testing will take the QA landscape to the next level. Although the process could be tricky with full of emerging technologies, discussions, and platforms coming up, adopting autonomous testing can help overcome the software development hurdles of the digital age. That being said, integrating AI with automated QA tools creates a self-healing and intelligent solution that can benefit businesses in the long run. It will also allow QA teams to become free from redundant tasks and focus on more strategic and result-driven tasks. Contact our experts now to find out how Tx can assist with autonomous testing.

The post How Autonomous Testing is Transforming the Software QA Landscape first appeared on TestingXperts.

Acceptance tests are one of the best QA strategies for ensuring a quality-centric and consistent software development lifecycle. However, in an agile environment, the time needed for their implementation must not hinder the timeline of functionality delivery, and the code must comply with the organization’s technical guidelines. A sub-branch of TDD, Acceptance Test-Driven Development (ATDD), keeps the customer first by making acceptance test cases the core of the software development cycle. It allows teams to create acceptance test cases before the code generation stage. Developers use these test cases as a reference for coding, and if they fail in these test cases, it means standards have not been followed.  

What is Acceptance Test-Driven Development (ATDD)?

Acceptance test-driven development (ATDD) follows the same principles of TDD (test-driven development) and involves creating acceptance test cases before implementing new software functionality. It represents the user’s POV (point-of-view) and validates whether the software is working as intended. ATDD is somewhat similar to BDD (behavior-driven development) except for a small difference, i.e., BDD focuses on the system behavior while ATDD focuses on customer requirements.  

ATDD has multiple team members with different roles and work in collaboration. For example, QA, product owners (POs), and developers collaborate to generate acceptance criteria. The criteria are converted into acceptance tests, which validate system usage scenarios. These tests follow the “Failure – Refractor – Success” cycle until delivery. TestNG, easyB, Thucydides, Concordion, FitNesse, and Cucumber, are some of the ATDD tools software development businesses use. 

4 Stages of ATDD

Stage 1: Creating User Story:

The process starts with creating a user story that shows the user’s expectations at the end of development. It is then implemented to meet the user objectives. 

Stage 2 Creating Acceptance Tests:

The first stage output is the input for drafting acceptance test criteria. These test cases will consist of all the real-world scenarios and how the system would react in each scenario. 

Stage 3 Test Case Automation:

The third stage automates the acceptance test cases by turning them into an executable format and loading them into a testing tool. The QA teams usually use Concordion, FitNesse, and Cucumber tools for ATDD automation.  

Stage 4 Implementation:

QA and dev teams use acceptance test criteria as a core for software development. This may involve leveraging the TDD model to code, test, and refactor until they get the go-ahead from the test case. After several iterations, they develop new features and release them.  

Difference between ATDD, BDD, and TDD

Business Benefits of Acceptance Test-Driven Development (ATDD)

Prioritizing User Requirements:

User acceptance test criteria are the basis of the development cycle, so customer requirements and objectives remain the prime focus throughout the process. Teams can hypothetically predict the end result for better coding and unit testing. ATDD encourages developers to think from end-user perspectives and prioritize their requirements at every development stage. 

Better Collaboration Among Stakeholders:

Collaboration begins from the initial stage i.e., user story creation, and continues till the code fulfills the user acceptance criteria. Business analysts, product owners, developers, and testers collaborate to work throughout the development process. ATDD gives a clear picture of the main objectives and ensures better compliance with requirements.  

Easily Manageable:

ATDD projects consist of small iterations and require a small team to manage. This means the teams are often small and consist of experienced individuals who have the right skillset related to the job. With Acceptance Test-driven Development (ATDD), businesses can easily manage team infrastructure and resources and would have a better rapport and collaboration than larger teams. 

Resolving Issues Faster:

Compared to regular development cycles, ATDD is an integral part of the process and is performed multiple times to ensure that the code perfectly aligns with the user’s expectations. It is not an isolated strategy meant to be run before rollout. Businesses can easily identify and resolve issues early before they become a nuisance.  

4 Popular ATDD Frameworks

FitNesse:

FitNesse is a web server, a wiki, and an automated testing framework for software testing that enables teams to write acceptance tests in a collaborative environment. It’s highly iterative and integrates perfectly with multiple programming languages. 

Key Characteristics: 

• Teams can write tests in tabular format. 
• Language-agnostic and supports multiple libraries 
• Extensible via custom keywords and plugins.

Cucumber:

Although it’s a BDD framework, Cucumber can also be leveraged for ATDD by generating acceptance criteria in the Gherkin language.  

Key Characteristics: 

• Utilize the “Given-When-Then” format for test scenarios. 
• Highly compatible with languages like Ruby, Python, and Java. 
• Facilitate communication between technical and non-technical teams. 

SpecFlow:

SpecFlow is a .Net-based framework that brings Gherkin-based acceptance testing to the Microsoft Stack. It allows users to define acceptance criteria in Gherkin syntax and is ideal for .Net projects. 

Key Characteristics: 

• Integrates with Visual Studio and other CI/CD tools. 
• Facilitates collaboration by writing plain-text test cases. 
• Support both BDD and ATDD practices. 

Robot Framework:

Robot Framework is an open-source automation framework that supports Acceptance Test-driven Development (ATDD) through a keyword-driven testing approach. Teams use this framework to run acceptance tests for web applications, APIs, and more. 

Key Characteristics: 

• Enable test case generation in tabular format. 
• Language-agnostic framework and supports multiple libraries. 
• Extensible via custom keywords and plugins. 

Summary 

Acceptance Test-driven Development (ATDD) integrates acceptance testing within the software development lifecycle to align end results with user expectations. This approach facilitates better stakeholder collaboration and uses the “Failure-Refactor-Success” cycle for iteration. It allows businesses to deliver high-quality, user-focused solutions, driving value and promoting agile development success.  

At Tx, we focus on delivering high-quality software despite time constraints in the development and QA environment. We offer distributed Agile test services to enable our clients to leverage the benefit of robust QA services in today’s tech-driven business environment. Contact our experts now to know more about our agile testing solutions and how Tx can help. 

The post How Acceptance Test-Driven Development (ATDD) Drives Business Value and Growth  first appeared on TestingXperts.

Businesses often struggle with the output of their testing processes even though they thoroughly go through every QA stage. The primary cause for this is insufficient QA reporting and analytics. Since the global software testing market is projected to hit $109 billion by 2027, the need for robust QA practices is of the essence for businesses. However, without strong reporting and actionable items, it becomes difficult for QA teams to identify improvement areas, track quality metrics, and align their strategies with business goals.

The software QA process should be transparent, and timely reporting is critical for tracking product health and facilitating accurate decision-making and planning. A high-quality QA report would ensure that a project meets defined standards at every SDLC stage, enabling businesses to deliver reliable, high-performance products.

What is QA Reporting?

Software testing and quality assurance will be critical elements in a successful product launch as businesses shift to digital solutions. QA includes multiple components, from test plan development and execution to debugging and root cause analysis. Every step is crucial for building confidence in the final product. But what helps keep track of all these steps and project development progress? The answer is “QA Reporting.”

QA reporting is a detailed document of testing activities involved in software development and QA lifecycle. It consists of details regarding tests performed, output, number of defects identified, and a comprehensive analysis of all these factors. QA reports deliver a complete view of the software quality at a given time interval.

Why and When do Businesses Require QA Reports?

QA reports are essential for software development. They allow businesses to gain detailed insight into their product/service quality. These reports also help identify issues and areas for improvement to ensure compliance with industry standards. Let’s look at some of the scenarios where QA reporting is required:

Development Phase:

To track project progress against quality standards defined in the planning stage, thus facilitating early issues detection and remediation.

Pre-release Phase:

To inform stakeholders about the software product’s readiness, highlighting any underlying defects that require immediate action and assessing overall quality.

Post-release Phase:

Document issues arising in the production phase and their fixes while providing feedback for continuous improvement.

QA reports also assist with stakeholders’ expectations management by offering a realistic view of software quality. They also support better business decision-making with their data-driven insights regarding product development and release.

5 Things to Include in QA Reporting

A structured QA report consists of several elements providing a clear overview of testing processes and results. Here are five essential elements to include:

Project Stakeholders:

A QA report begins with a project summary covering the software version, development phase, and relevant background to set the context. It clearly defines the scope, objectives, and focus areas of testing. Key performance indicators (KPIs) like defect density and test coverage will help measure success. Resource allocation, including testing teams, tools, and constraints, will also be included. Lastly, highlighting risks, issues, and mitigation strategies will ensure transparency and better decision-making.

QA Objectives:

Clearly define test objectives and align them with project goals to measure success rate. This will include executed test cases with brief descriptions to showcase the testing scope. The entry and exit criteria will define the start and end of the QA phase. Documenting the test environment (hardware, software, and network configurations) will also support result reproducibility and issue diagnosis.

Test Approach:

Outline the test approach, detailing manual, automated, or hybrid testing methodologies. Also, specify the testing methodology used, whether Agile, Waterfall, or DevOps, as it structures the testing process. Additionally, listing the types of testing performed—such as functional, performance, security, and usability— will help with a comprehensive view of software quality.

QA Output:

A QA report will provide a clear breakdown of testing output. It will also list each test case’s result (pass, fail, or block). Defects will be documented with details on severity, status, and any noticeable patterns. Comparing actual results against key performance indicators (KPIs) will allow businesses to assess how well testing met their project goals.

Recommendations:

The QA report will highlight key takeaways from the testing process, such as insights on testing strategies, resource use, and collaboration. Any unexpected findings or new risks will be noted to improve future efforts. Recommendations will focus on software enhancements, process improvements, or timeline adjustments. A summary of overall quality will indicate readiness for the next phase, whether further development, release, or monitoring.

Consequences of Inadequate QA Reporting

• Lack of transparency in the quality assurance process will prevent teams from tracking the accountability of each testing phase. It would also complicate audits, give rise to compliance issues, and reduce accountability.

• Not having clear visibility across improvement areas will cause inefficient resource allocation, while the team might focus on low-priority issues and neglect high-risk modules.

• Identifying patterns, defects, root causes, and areas that require urgent action will become challenging. This will lead to repeated mistakes as teams cannot learn from previous mistakes.

• Stakeholders will lose confidence in testing if QA reports do not deliver valuable insights or contribute to the project’s success.

• Consistent project delivery delays, increased defects, and inefficient QA practices will increase development costs.

How to Build an Effective QA Report?

Businesses should focus on actionable steps to optimize their testing process. This will be possible by following the below-given recommendations:

Create a QA Report Outline:

Start by drafting a clear outline that structurally guides the readers through the QA process, outcomes, and conclusion. This will ensure that no detail is ignored and that the flow is smooth between sections.

Use Easy-to-Understand Language:

Multiple stakeholders with technical and non-technical backgrounds will read the QA report. So, the language must be user-friendly and easy to understand, giving a clear picture to all. Avoid using technical jargon; otherwise, provide a detailed explanation if necessary.

Use Visuals for Clarity:

Insert visuals like screenshots, graphs, and flowcharts to make complex information more engaging and easy to understand. For example, screenshots can show specific issues or test results, while graphs show defect trends to showcase software quality improvement.

Why Partner with Tx for Comprehensive Testing?

QA reporting is a critical component in the software development and QA process. It offers clear, data-driven insights that influence stakeholders’ business decisions. Teams can assess software readiness, upscale QA efforts, and streamline product release cycles. At Tx, we follow a robust QA reporting framework that provides real-time dashboards, actionable items, and defect details. It enables our clients to improve their software quality and mitigate risks. Here’s what Tx offers:

Global Expertise:

Our skilled QA professionals and a strong presence across multiple geographies bring in-depth industry knowledge to deliver seamless testing solutions.

AI-driven Testing and Reporting:

Our AI-powered test automation, intelligent dashboards, and predictive analytics assist with software quality enhancement and issue resolution.

Test Automation Expertise:

Our end-to-end test automation solutions help reduce QA TCO by 40-50%.

E2E Testing Solutions:

We cover all QA needs, from functional to automation testing to performance and security checks.

Summary

QA reporting ensures software quality by providing real-time insights and aligning testing strategies with business objectives. It assists businesses in identifying issues, optimizing resources, and improving decision-making. Tx offers AI-driven reporting and industry-leading digital assurance solutions to enhance your software product reliability. Organizations can streamline testing, accelerate releases, and deliver high-quality digital experiences with Tx’s robust QA frameworks. To learn how Tx can assist you, contact our experts now.

The post How Does Effective QA Reporting Drive Business ROI and Growth first appeared on TestingXperts.

Contents

1. Role of Quality Assurance
2. Domain Specialization and QA
3. Advantages of Domain Specialization in QA
4. Conclusion

Role of Quality Assurance

Quality Assurance practices over the past years have experienced many waves of innovation, be it test automation or methods to optimize testing. With changing times, the QA function as we knew it transformed and the focus shifted to:

– Meeting the essential compliance and regulatory standards

– Requirements coverage referring to code coverage

– Giving utmost importance to customer experience

Earlier, QA industry had a horizontal focus and was indifferent to business domains. However, it is turning out to be an industry where domain specialization is a must. Therefore, QA teams need to have profound knowledge of the business domain while testing an application. For QA teams to be considered as quality gatekeepers, their understanding of the domain must be up to the required standard.

Domain Specialization and QA

Domain knowledge can add an advantage to the testing project by helping testers to prioritize the features for test execution and exercise underlying business rules. Domain specialized experts can test an application in the most anticipated way, according to the end user perspective. Domain knowledge can help Quality Assurance teams achieve multiple benefits throughout the Software Development Lifecycle.

Advantages of Domain Specialization in QA

Here is the list of the advantages of having domain specialization:

1. Quick requirement analysis:

Having a domain specialization helps identify gaps in the requirements, ensures better coverage from business functionality perspective and enables early identification of defects through static testing.

2. Better user story development:

A QA with good domain knowledge can help the product owner by writing better user stories and defining better acceptance criteria.

3. Builds capability:

Domain knowledge helps a QA engineer to have a better view of the product. Without proper domain knowledge, defects in the underlying business rules can remain uncovered in the preliminary stage of testing, causing problems during the final or release stage.

4. Thorough understanding of technical terminology:

An engineer who completely understands the domain can detect issues early as compared to a novice in domain knowledge. A proper understanding of the technical terminology will help the tester to describe the problem and design edge case scenarios accurately.

5. Helps in prioritizing bug fixes:

With proper knowledge, the tester will have a clear idea of how to prioritize the bug fixes and can guide rest of the development team. While planning the bug fixes, the tester will not just save cost and time but will also enhance the overall product a quality in cost-effective manner.

6. Reduces training time:

Domain specialization can increase the overall productivity of the team and add better value to the final product.  Having prior knowledge of the domain will reduce the training time, resulting in quicker results. Proper knowledge of the flow of the business processes helps in achieving a better understanding of the product requirements.

7. Boosts the productivity of the team:

Domain specialists are in demand due to their ability to understand the application beyond finding bugs. Having domain knowledge can help in suggesting and implementing fixes in crucial times. This not only saves time, but also increases the productivity of the team.

Conclusion

Emphasizing on domain specialization in QA is bridging the gap between business users and the IT teams, thus increasing the relevance of the work product delivered to the end user. Domain specialization is creating a platform for innovation by evolving the QA practices.

TestingXperts’ domain-based testing team of QA professionals have extensive experience in the latest technologies across various business verticals such as Healthcare, Insurance, Manufacturing, logistics, Banking and Financial Services, etc. While being well versed in the latest testing trends, TestingXperts’ team come with great domain knowledge and skill sets. TestingXperts’ domain-based testing ensures delivery of the expected services and enables the higher customer satisfaction.

The post Achieve Next-Level of QA Innovations with Domain Specialization first appeared on TestingXperts.

Content

1. Top Nine Insurance Industry Trends To Watch Out for

1.2 Distributed infrastructure

1.3 Digital transformation

1.4 Automation

1.5 RPA

1.6 Low/No Code Development

1.7 Artificial intelligence (AI)

1.8 Predictive analytics

1.9 Connected insurance internet of things (IoT)

1.10 Chatbots

1.11 Blockchain

2. Conclusion

Thriving within the insurance industry and providing the customers with a distinct user experience comes from adopting a holistic digital mindset-which means staying updated with the insurance industry trends and keeping the business technology strategy updated. Evidently, following the latest trends enables businesses to prepare a roadmap to meet the needs of the evolving insurance landscape.

Top Nine Insurance Industry Trends To Watch Out for

From digital transformation and automation to IoT and chatbots, here are the top insurance technology trends to watch out for in 2025 and beyond.

Distributed infrastructure

Insurance providers worldwide have a significant technology debt as many core business processes still run on on-premises legacy technologies. As more and more business processes move to the cloud, insurance businesses can create and launch new products in faster and more cost-effective ways. Cloud will also enable insurance providers to collect, store, and analyze extensive customer data, such as tens of thousands of claims data points. As ecosystems continue to develop globally, cloud-native insurance providers will be positioned as central hubs among customers, carriers, healthcare providers, vehicle providers, smart home device distributors, and so on.

Digital transformation

This year, policyholders’ spend on technology and IT services is predicted to be $255bn, showcasing the oncoming wave of digital transformation. As a result, proactive insurance providers will develop new capabilities at every customer touchpoint, such as customer support, claims management, transactions, and overall customer experience enhancements.

As the business benefits of digitized insurance services rise across the insurance industry, insurance service providers will gain confidence and invest in digital transformation to offer an unmatched customer experience to their policyholders. Consumers are active on many digital channels and have become used to seamless user experiences – they expect the same while searching for insurance policies. Adopting to multi-access approach in every channel should be a key consideration for insurers in 2025.

Automation

As in the case of other industries, implementing automation in the insurance sector can enable quick, effective, and scalable business processes. Robotic process automation (RPA) is ideal for optimizing many insurance-related operations, such as claims management, automated payments, compliance management, underwriting, policy management, and so on.

ZhongAn, one of the leading insurance companies in China, achieved 99% automation for underwriting and 95% for claims settlement. These stats highlight how automation can be a smart way to streamline insurance business operations in 2025 and beyond.

RPA

With the complex integration of the latest technologies within the insurance sector and the evolving mix of systems, there are a lot of repetitive and manual processes that are ideal candidates for RPA. Also, with rapid mergers and acquisitions happening in the insurance industry, parent companies are looking for innovative ways to convert the existing manual processes into time-efficient automated processes to achieve greater process efficiency. Here, RPA offers the best possible solutions to boost the operational efficiency of insurance businesses by automating repetitive business tasks with a software bot, thus eliminating human intervention.

Low/No Code Development

For insurance businesses to keep up with the cut-throat competition, their business processes must be efficient to manage advanced platforms, deploy updates faster and at scale, and release new products as quickly as possible. Low code/no code development enables simplified and robust configuration, development, testing, and management of insurance apps while ensuring increased productivity and improved customer experiences.

Artificial intelligence (AI)

AI is significantly making its mark in the insurance industry and is one of the key trends on the list. AI is transforming critical insurance processes such as underwriting, pricing, and claims. It is also one of the areas where rigorous research and development is taking place to enhance further business processes such as decision-making, cost optimization, and improving customer experience. Fraud detection is one key area where insurance companies leverage AI.

During the pandemic, fraudulent insurance claims in the U.K. grew by 10%. By integrating AI into the existing business process, insurance companies can reject transactions for false claims and even highlight such cases for further investigation.

Predictive analytics

In 2025, insurance organizations and distributors will be able to do much more with their data. This is especially important for sales and underwriting, in which the sales team can process data via AI and ML. During quoting, insurers can then choose the best insurance policies and plans for individuals and groups, accelerating the sales process. Predictive analytics enable insurance agencies to collect customer data to understand and predict their behavior. Predictive analytics can also identify fraud risks, triage claims, anticipate trends, and so on.

Adopting predictive modeling and analytics has proven to enhance revenue models for insurance providers. A not-so-recent yet noteworthy study from Valen Analytics found that insurance companies that utilized predictive analytics improved their loss ratios by up to 9 and raised their direct written premiums by 53%.

Connected insurance internet of things (IoT)

Connected insurance is a progression of the legacy insurance model. Connected car insurance is an example of insurance premiums based on the driver’s behavior. Benefits to insurance providers include improved driving behavior, reduced fraud, reduction in operational costs, improved CX, etc. Most insurance holders are willing to share additional personal data to save money on their insurance policies. IoT can automate the data collection from IoT devices such as smart homes, smart fire alarms, fitness trackers, automobile sensors, etc. to better determine rates, mitigate risk, and even prevent losses in the first place.

IoT will bolster other insurance technology with first-hand data, improving the accuracy of risk assessment, giving insureds more power to impact their policy pricing directly, and insurers the opportunity to improve accuracy and revenues.

Chatbots

Chatbots today are executing most of the customer interactions for digital businesses. By leveraging AI and ML, chatbots can seamlessly interact with customers without any human intervention. Ultimately, insurance companies can save time and money by integrating chatbots instead of a full-blown customer support department. A bot can guide a customer through filling up forms for signing up for a policy or raising claims, while humans can be reserved for other complex business needs. Insurers can reduce their operational costs by adopting chatbots or digital assistants.

Blockchain

Blockchain facilitates the establishment of an immutable digital ledger. Leveraging this innovation, insurers can curtail administrative expenses associated with claims assessment and the verification of third-party payments. Blockchain guarantees the secure sharing of such data, shielding it from fraudulent activities while offering ease of verification.

As stated by PWC, blockchain holds notable promise for the reinsurance sector, streamlining intricate processes and potentially yielding global savings in the range of USD $5-10 billion. A case in point is healthcare reinsurance, where the adoption of intelligent blockchain contracts could expedite the validation of consumer data and insurance records, thereby mitigating the typically convoluted exchanges.

Furthermore, blockchain’s capability for widespread distribution comes without the concern of duplication, affording enhanced transparency and bolstered governance over workflows

Conclusion

Keeping up with the latest insurance industry trends enables insurers to stay ahead of the competition and deliver as per the latest customer expectations. The tech trends mentioned above will reshape the insurance industry for good, creating space for innovation and opportunities. These evolutions are encouraging insurance providers to expand their capabilities by putting these trends and their implementations on the business agenda for 2025 and beyond.

TestingXperts (Tx) is extensively helping global insurers in their digital transformation and allowing them to provide unmatched customer experience. Tx’s testing services portfolio and unparalleled track record have proved it a preferred technology partner for insurance clients. Connect with us to learn more about our testing services specific to the insurance industry..

The post Top 9 Insurance Industry Trends to Watch Out In 2025 first appeared on TestingXperts.

The medical device software continues to become more complex due to various advances in healthcare. There are certainly important and compliance factors such as regulatory delays and the adoption of international standards that affect the medical device’s software quality.

Contents

1. What is Medical Device Testing?

2. What is the Role of QA in Medical Devices Testing?

3. What are the Methods Followed by QA for Medical Device Software Testing?

4. What are the challenges Faced with Medical Device Testing?

5. Conclusion

What is Medical Device Testing?

Medical devices are regulated mainly by multiple regulatory bodies and have to satisfy a set of compliances and standardizations. Moreover, they should deliver effective performance, accurate readings and should be safe while at use.

Therefore, it is essential for medical devices to have a proper device testing strategy in place. The approach consists of maintaining compliance processes and planned software testing approach to deliver device effectiveness.

Medical Device testing ensures the product’s software and devices are fully compatible and compliant with various regulations and functionality. This testing ensures that these devices fulfill all quality-related aspects successfully for which QA plays an important role.

Hence, medical device manufacturers should adopt quality assurance (QA) for thorough verification and validation of devices. It ensures to deliver the proper quality and reliability of these devices.

What is the Role of QA in Medical Devices Testing?

1. Ensures compliance testing with various regulations such as HIPAA, PCI DSS, and FDA, etc.
2. Ensures healthcare CRMs, patient mobile apps, medical devices software, and Patient Database management systems work seamlessly under various scenarios
3. Integration testing of devices ensures proper connectivity with other devices
4. Helps to improve the clinical effectiveness of medical devices
5. Adequate testing ensures the quality and effectiveness of various medical devices to provide optimal patient care
6. End-to-end devices testing mitigates any risk of device failure

Further, glitches in medical device software may lead to false diagnosis and hence QA is essential to ensure proper performance and functioning of medical devices.

What are the Methods Followed by QA for Medical Device Software Testing?

Having an effective testing strategy helps to achieve successful medical device testing which is based on several sets of test requirements. These requirements are based on various component specifications, manufacturing processes, and other critical functional specifications of the devices and software.

Further, proper evaluation of the sets of requirements smoothens the test implementations as tests can be carried out effectively on the software of medical devices.

Different parameters have to be tested to ensure device functionality. There are various methods for medical device software testing.

1. Functionality Testing:

It focuses on test efforts to evaluate how the device is supposed to perform under various scenarios. It involves the actual process of verification and validation of devices. Each of the functions is exhaustively tested with a combination of parameter values to know the device’s functionality.

2. Device Verification Testing:

This process confirms whether the examination of any objective evidence with specified requirements of the device has been fulfilled or not. The process starts with clear and well-defined product requirements which must be measurable in order to verify them.

The process follows certain protocols and uses a standard methodology that compares current product requirements with already approved requirements.In case, if the device verification process fails, then it is essential to undergo further investigation to find the requirement that couldn’t meet the expectation. Then, a further investigation will help to sort out whether the requirement can be updated or not.

A perfect medical device strategy can save money for the enterprises and ensures the device meets the required expectations with the least device risks and reduced testing time.

3. Device Validation (Software/Hardware) Testing:

After the entire system is in place, then medical devices should be validated for both software & hardware. The primary purpose of this validation process is to test if the device meets specific user needs or not. It can be tested under simulated or under actual environment conditions to know how the software and hardware function.

Unit testing is the initial approach that is practiced by considering a set of stages or blocks in the process. Later, the entire system is validated as a whole. However, based on the system complexity, it can be a daunting practice for the automated test system. In such cases, the efficient way is to adopt a divide and validation approach.

4. Device Compatibility & Error Handling Testing:

Medical device’s compatibility across various devices has to be tested to prove its success. It is essential to test how the device responds under various error conditions especially under power fluctuations, internal failures or for any peripheral device failure. The devices should deliver proper error messages to notify users.

5. Automated Testing:

This form of testing is taken up to ease the testing process and overcome manual tasks to ensure quality at speed. The automated testing makes the qa process faster, more accurate and complete. Primarily, the process includes stimulating the target device, monitor the device response, record the results, control the entire process using automation and declare results.

While opting for the test automation system, the testing teams need to adopt the strategies based on the test options achieved in each medical device development stage. In case, the testing strategy is for complex devices that use extreme voltage and current, then it is essential to break down the test specifications into hardware and software requirements based on the interfaces as hardware and software. This will prevent the testing teams from facing any sort of challenges in the test automation environment.

6. White-Box Testing:

Automation of white-box testing is essential as it ensures the testing process to be closer to the design and coding phases of the project. It is necessary to introduce an automated test system that enables an inside look at medical device software. Tests are devised to monitor various parts to check any overflow conditions, timing interactions, interrupt handling, error counts, etc.

What are the challenges Faced with Medical Device Testing?

Challenge with Device Compatibility:

Testing the healthcare apps for web and mobile compatibility is one of the important challenges faced in medical device testing. In the present world, most of the people rely on mobile platforms for almost every need. Hence, it is essential to optimize the healthcare app for several mobile devices and different operating systems. It is also essential that the apps should be easily accessible, provide the security for every information provided by the user, and at the same time should help to receive every required update.

Challenge to overcome security breaches:

Security is one of the important requirements for every medical app. With the rampant cyber-attacks on various apps, any security breach will cost the company some fortune. Hence, to avoid such scenarios, it is important for businesses to leverage security testing by the next-gen testing services provider.

They have the right skills set and security testing tools for enabling effective security testing and penetration testing of these medical devices apps. Therefore, this way of adopting security tests in every phase of app development will effectively help healthcare devices meet the required security requirements.

Challenge with Cloud Adoption:

Every healthcare enterprise of the present world is significantly shifting towards the Cloud. This way of shifting to the cloud is increasing challenges with respect to network and data security of these medical devices testing. If enterprises fail to overcome these challenges, then it might adversely affect their brand. Hence, to overcome these scenarios and to handle the evolving challenges, it is required for enterprises to adopt the practices of cloud application testing and security testing during data migration to ensure a more secure network and safety to cloud data. With trained QA resources from an independent testing services company, it becomes easy for businesses to embrace cloud

Conclusion:

It is a known fact that medical devices are critical as they have a direct impact on patient’s healthcare. To ensure optimal working of medical devices an end-to-end medical device software testing is essential.

Any sort of performance issues prevailing in these devices proves to be fatal to the patient’s healthcare. Thus, to ensure fully-functional medical device software, businesses should adopt effective software quality assurance.

The post Why Quality Assurance is Critical for Medical Device Testing? first appeared on TestingXperts.

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