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.

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1. The Role of QA in Digital Transformation
2. Common Challenges in Digital Transformation QA Process
3. QA Strategies for Successful Digital Transformation
4. The Impact of QA on Project Outcomes
5. List of Cybersecurity Automation Tools and its Use Cases
6. Conclusion
7. Why Partner with TestingXperts for Digital Transformation QA?

Digital transformation is not just a trend but a fundamental shift crucial for businesses to maintain their competitive edge in a rapidly evolving marketplace. It encapsulates integrating digital technology into all business areas, fundamentally changing how they operate and deliver value to customers. It’s a complex strategy beyond mere automation to reimagine business models and processes for the digital age fundamentally.

Yet, digital transformation is a complex process with significant hurdles. According to statistics, 20% of organizational change failures are due to ineffective communication, and 32% are due to poor project management. Within this transformation process, Quality Assurance emerges as a crucial step, ensuring that digital initiatives are technologically proficient and aligned with strategic business objectives.

The Role of QA in Digital Transformation

Quality Assurance has undergone a significant transformationy, becoming an integral component of the development lifecycle. As technologies advance and consumer expectations rise, QA methodologies have had to adapt, becoming more agile and proactive in identifying and addressing issues. In this process, QA professionals are entrusted with the critical responsibilities of mitigating risks, guaranteeing product quality, and validating the fulfilment of user requirements. Their role includes participation in the early stages of project conception, planning, and the continuous delivery pipeline.

In digital transformation projects, the role of QA is to act as the shield of quality and functionality. Also, the QA teams shoulder a multitude of responsibilities in digital projects, which include:

Defect Prevention

QA teams must proactively identify and resolve potential issues before they manifest in the final product. This involves rigorous testing protocols throughout the development cycle, from the initial design to post-deployment. They also work closely with the development team to foster the best coding and project management practices that minimize the risk of errors.

Quality Control

The QA team performs systematic checks and balances to ensure that every aspect of the project adheres to the highest quality standards. This extends beyond software functionality to include user experience, performance, security, and compliance with regulations and industry standards. Quality control is an ongoing process, with continuous testing at various stages of product development.

Risk Management

Risk management requires QA teams to identify, analyze, and prioritize potential risks to project success. This involves creating test cases that simulate unusual or unexpected conditions and planning for contingency actions. The goal is to minimize the impact of risks on the project timeline, budget, and outcome quality.

Continuous Improvement

QA teams support continuous improvement, applying lessons from current and past projects to refine processes and methodologies. They utilize metrics and feedback to drive enhancements in the QA process and the wider project execution.

Stakeholder Communication

Effective communication with stakeholders is critical. QA teams must communicate complex information clearly and concisely to ensure stakeholders are informed about the quality status and any issues that arise. They are also responsible for gathering stakeholder feedback to inform the QA process.

Strategic Input

QA teams provide strategic input by aligning the QA process with business objectives and customer expectations. They contribute insights that influence the direction of project development, ensuring that the product delivers value and effectively addresses market needs.

Common Challenges and Solutions for Digital Transformation QA Process

QA teams encounter specific challenges that can hinder progress and impact the quality of the final product. These challenges range from adapting to new technologies and methodologies to ensuring seamless integration with legacy systems. QA teams must employ innovative solutions and adhere to best practices that address these technical difficulties and enhance the overall efficiency and effectiveness of the transformation process. Following are some of the challenges that occur during the digital transformation testing process:

Adapting to Agile and DevOps

The shift from traditional Waterfall approaches to Agile and DevOps necessitates a cultural and procedural change within QA teams. This transition involves adopting a mindset of continuous testing, integration, and deployment. In Agile, testing is not a phase but an ongoing activity. QA teams must work closely with development and operations to ensure that testing is integrated at every stage of the software development lifecycle (SDLC). This requires QA specialists to develop new skills in test automation and continuous testing to keep pace with rapid release cycles.

Legacy System Integration

Digital transformation involves a complex integration between new and existing systems. Integrating cutting-edge technologies with legacy systems is challenging due to compatibility issues, differing data formats, and the potential disruption of business processes. QA teams play a critical role in ensuring seamless integration, which often involves creating customized testing strategies to verify that legacy systems continue to function correctly alongside new technologies.

Cloud-based Testing Complexities

Cloud computing has revolutionized how applications are developed, deployed, and tested. However, the cloud’s distributed nature introduces complexities such as ensuring consistent performance across different environments and managing data security and privacy. QA teams must adopt cloud-specific testing strategies that can accommodate the dynamic scaling of applications while maintaining performance benchmarks. Moreover, they must stay up-to-date with cloud service providers’ tools and services to leverage them effectively in testing activities.

Security Risks

As software becomes increasingly interconnected and data-driven, the attack surface for potential security threats widens. QA teams must incorporate comprehensive security testing into the SDLC to identify vulnerabilities early. This includes conducting regular security audits, penetration testing, and employing static and dynamic code analysis tools. QA professionals must have a strong understanding of cybersecurity best practices to safeguard applications against threats effectively.

Technological Advancements

The rapid evolution of technology presents opportunities and challenges for QA teams. New tools, platforms, and methodologies emerge constantly, requiring QA professionals to learn and adapt continuously. Keeping up with these advancements is crucial for selecting the right tools and approaches for testing activities. However, it can also be overwhelming and lead to a skills gap if not appropriately managed. QA teams must prioritize learning and development to build expertise in new technologies and testing methods.

QA Strategies for Successful Digital Transformation

Effective QA strategies ensure digital transformation efforts deliver optimal results and align with business goals. These strategies encompass a comprehensive approach to testing, where modern tools and technologies are crucial in streamlining and enhancing QA practices. As organizations upgrade to the digital landscape, integrating cutting-edge QA tools and adhering to proven QA methodologies is imperative.

Establishing Effective QA Processes

QA Framework Alignment

QA frameworks must reflect the organization’s goals. It involves tailoring testing strategies to support the unique needs of digital transformation projects, which may include rapid deployment cycles, complex integrations, and new customer experiences. A well-aligned framework guides QA activities and ensures they support the overall objectives.

Culture of Quality

Establishing a quality-focused mindset across all teams ensures everyone contributes to the QA process. This culture shift means quality assurance is considered at every project stage, from planning and development to deployment and maintenance, enabling a proactive approach to quality.

Roles and Responsibilities

A clear understanding of QA roles helps prevent overlap and confusion. It involves defining who is responsible for writing test cases, who manages automation, who conducts security testing, and so on. This clarity promotes accountability and ensures that all QA tasks are covered effectively.

Diverse Testing Types

Different types of testing provide safety measures at various stages of software development. Unit testing checks individual components, integration testing ensures components work together, system testing evaluates the complete system, and acceptance testing confirms the product meets business requirements.

Continuous Testing Integration

Integrating continuous testing into the development pipeline enables immediate feedback on the impact of changes. By automating and running tests with every build, teams can detect and address issues before the product reaches production.

Bug Tracking and Resolution

Efficient bug tracking, and resolution processes are vital. It involves using tools to log, track, and manage defects so that they can be prioritized based on severity and impact. The process should be transparent and collaborative to ensure timely fixes and prevent bug leakage to production.

Tools and Technologies in Modern QA Practices

Automation Tools

Tools like Selenium or Appium help execute test cases without manual intervention, saving time and reducing human error. They can run tests consistently and quickly across different environments and platforms, providing robust feedback.

Test Management Software

Software like Zephyr or TestRail helps organize test cases, manage test runs, and track results. It centralizes information, making it accessible to all team members and allowing for efficient management of the QA process.

Performance and Security Testing Tools

Tools such as LoadRunner, Neoload and Jmeter for performance testing and OWASP ZAP, Burpsuite and Veracode for security testing help ensure that applications can handle the expected load and are secure from vulnerabilities. These tools simulate user behavior and attack patterns to test the robustness of applications.

Virtualization and Containerization

Technologies like Docker and Kubernetes allow teams to create, deploy, and manage testing environments that mimic production setups. This reduces the risk of environment-specific bugs and streamlines the process of setting up and tearing down test environments.

Analytics and Reporting Tools

Analytics tools provide insights into the QA process’s effectiveness, highlighting improvement areas. Reporting tools visualize data on test coverage, defect densities, and other key metrics, helping teams make informed decisions about quality.

The Impact of QA on Project Outcomes

Rigorous QA practices are necessary to elevate product quality and ensure digital initiatives meet and exceed performance expectations and user satisfaction benchmarks. By embedding QA into the digital transformation project lifecycle phase, from initial design to final release, organizations can mitigate risks, reduce costs associated with post-deployment fixes, and deliver solutions that genuinely transform their operational capabilities. Let’s look into how a thorough and strategic approach to QA can significantly enhance the value delivered by digital transformation efforts, leading to definite benefits for businesses and end-users:

Ensuring Alignment with Business Goals

QA teams align software features and functions with the company’s strategic goals, ensuring that the final product works flawlessly and delivers the promised business value. This alignment is crucial in realizing the return on investment and driving business growth.

Risk Mitigation

In digital transformation, the stakes are high, and the complexity of new technologies can introduce significant risks. A thorough QA process proactively uncovers errors, inconsistencies, and bugs early when they are less expensive and easier to fix. This early detection prevents minor issues from escalating into larger failures, thus protecting the project from costly delays, budget overruns, and potential cancellations.

Compliance and Security

When going through digital transformation, businesses must comply with many regulations and standards, which can vary by industry and region. Rigorous QA processes ensure that products comply with these regulations and adhere to best practices for data protection and privacy. This is especially critical in industries such as finance and healthcare, where non-compliance can lead to significant legal penalties and loss of customer trust. Furthermore, comprehensive security testing as part of QA helps uncover vulnerabilities and protect against breaches, safeguarding the organization and its customers from potential cyber threats.

Conclusion

Quality Assurance ensures that digital initiatives align with strategic business goals, mitigate risks, cut costs, and improve user satisfaction. Organizations that deploy thorough QA strategies implementing rigorous testing protocols and cutting-edge tools position themselves to manage digital transformation’s complexities effectively. As the technological landscape continues to advance, the significance of QA in guiding projects to successful completion is increasingly important, securing its place as an essential part of digital transformation efforts.

Why Partner with TestingXperts for Digital Transformation QA?

Partnering with the right QA provider is crucial for ensuring a smooth and successful digital transformation. TestingXperts, one of the leading Quality Assurance services providers, offers bespoke, innovative solutions tailored to the unique challenges of digital transformation. Our dedicated team of experts, state-of-the-art tools, and cutting-edge methodologies combine to deliver unparalleled value to our clients.

Key Differentiators

Expertise in Digital Transformation

TestingXperts brings a deep understanding of digital transformation with a team of specialists who have successfully worked with numerous enterprises through their digital evolution. This focused expertise allows for QA processes that are not just thorough but also aligned with the latest digital trends and industry best practices.

Advanced Test Automation Frameworks

Our proprietary test automation frameworks are designed to integrate seamlessly with modern CI/CD pipelines, supporting Agile and DevOps practices. This ensures our clients can achieve faster time-to-market without compromising quality, a key advantage in today’s fast-paced digital economy.

Custom AI-driven Analytics

TestingXperts leverages artificial intelligence to provide predictive analytics, enabling businesses to anticipate potential bottlenecks and quality issues before they impact the project. This proactive approach is critical in a landscape where digital transformation initiatives must be agile and reliable.

Robust Security Testing Protocol

With cybersecurity threats on the rise, our robust security testing protocols are ingrained into the QA process, ensuring that every digital solution is functional, user-friendly and secure against emerging threats. This comprehensive security stance is essential for maintaining trust in the digital age.

Comprehensive Digital Ecosystem Testing

Understanding that digital transformation encompasses a variety of technologies, TestingXperts offers comprehensive testing across the entire digital ecosystem, including cloud, IoT, big data, and more. This ensures a holistic QA approach covering all digital transformation journey aspects.

Collaborative Approach with Stakeholders

We believe in working closely with all stakeholders to ensure transparency and alignment throughout the QA process. Our in-house accelerators and dashboards provide real-time insights into the QA progress, facilitating informed decision-making and seamless communication.

To know more, contact our experts now.

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Table of Contents

1. Quality Assurance vs Quality Engineering
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 vs Quality Engineering

“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.

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Table of Contents

1. The Role of AI in Quality Assurance
2. Challenges of Implementing AI in Quality Assurance
3. Benefits of AI-Led Quality Assurance
4. AI Testing Tools for Quality Assurance
5. Conclusion
6. How can TestingXperts help transform QA using AI

Artificial Intelligence (AI) is drawing substantial financial investments and genuine enthusiasm from prominent entrepreneurs because of its promising advantages. Companies are eagerly adopting AI in quality assurance to streamline their operations and lower expenses. AI facilitates the efficient sharing of data, forecasts customer behavior, offers product recommendations, identifies fraudulent activities, tailors marketing to specific demographics, and delivers valuable customer support.

If you’re still uncertain about the practicality of AI, here are some astonishing statistics:

• 63% of companies acknowledge that the pressure to cut costs will drive them to adopt AI.

• 61% of business leaders assert that AI assists them in identifying business opportunities they might otherwise overlook.

The Role of AI in Quality Assurance

To evaluate software and its functionalities, it is essential to develop a test suite and generate test data. Nevertheless, this procedure is time-consuming. On average, when employing manual testing methods, it accounts for more than one-third (approximately 35%) of the total time expended during a testing cycle.

In addition to this, manual testing is susceptible to human errors, which can lead to increased expenses and consume more time and resources – something no business is eager to invest in.

Additionally, as software becomes more intricate, the number of tests naturally grows, rendering it progressively harder to maintain a test suite and ensure sufficient code coverage.

In such situations, leveraging artificial intelligence for quality control can expedite the mobile app testing procedure and surmount all the aforementioned challenges. The key areas where AI enhances testing processes in quality assurance encompass:

• Comprehensive supervision of API testing.

• Automation of testing operations.

• Determining the optimal timing and methodology for executing a test script.

• Utilizing AI-based spidering tools for more effective data analysis.

Here are a few limitations of manual testing and How AI can change that:

Challenges of Implementing AI in Quality Assurance

Implementing AI in Quality Assurance (QA) introduces a range of complex challenges that organizations must address to ensure the successful integration of artificial intelligence into their quality control processes. These challenges include:

Data Quality:

QA teams must grapple with the need for high-quality training data. Ensuring that the data used to train AI models is accurate, representative, and free from biases is a crucial initial step.

Integration Complexity:

Integrating AI into existing QA systems can be a daunting task. Ensuring compatibility, data flow, and proper synchronization with legacy processes is essential for a smooth transition.

Regulatory Compliance:

Depending on the industry, there might be specific regulations and data privacy laws that must be adhered to when implementing AI in QA. Ensuring compliance with these regulations is a critical concern.

User Acceptance:

Convincing QA teams and stakeholders of the benefits and reliability of AI-driven processes is essential. Education, training, and effective communication are required to gain trust and acceptance.

Benefits of AI-Led Quality Assurance

The utilization of Artificial Intelligence (AI) in software testing brings forth distinctive advantages that can significantly transform the quality assurance process. Let’s explore these benefits in detail, particularly focusing on how AI contributes in transforming Quality Assurance:

Speedy Timelines:

Efficient Test Execution:

AI-driven test automation can execute a vast number of test cases at unprecedented speeds, significantly reducing the time required for testing. Tasks that might take human testers days or weeks can be accomplished in a matter of hours.

Faster Regression Testing:

AI streamlines regression testing by automatically re-running test cases to check for unintended side effects. This ensures that new code changes don’t break existing functionality, enabling faster release cycles.

Well-Researched Build Releases:

Predictive Analysis:

AI can analyze historical testing data and software performance metrics to predict potential defects. By identifying areas of the software that are more likely to contain issues, teams can focus their testing efforts on high-risk areas, leading to more well-informed build releases.

Continuous Monitoring:

Real-time monitoring by AI can detect anomalies and performance issues in production, helping in pinpointing areas that require immediate attention before a new build release.

Going Beyond the Limits of Manual Testing:

Comprehensive Test Coverage:

AI can perform extensive and exhaustive testing, covering a multitude of scenarios and edge cases that manual testing may overlook. This thorough test coverage ensures that software is rigorously examined.

Scalability:

AI-powered testing is highly scalable, accommodating the increasing complexity and size of modern software projects. It can adapt to new technologies, larger datasets, and evolving testing requirements, something that manual testing often struggles to achieve.

Effortless Test Planning:

Intelligent Test Case Generation:

AI algorithms can autonomously generate test cases, making the test planning process more efficient. These generated test cases often cover critical paths, boundary conditions, and high-impact scenarios, saving time and effort in test case creation.

Test Prioritization:

AI can prioritize test cases based on their criticality and potential impact, simplifying test planning by ensuring that the most important tests receive primary attention.

AI Testing Tools for Quality Assurance

AI testing tools for quality assurance have gained popularity in recent years for enhancing testing efficiency and accuracy.

Applitools:

• Applitools offers Visual AI testing, a technology that uses AI and machine learning to automatically detect and analyze visual differences in the user interface of applications. It’s particularly useful for web and mobile app testing.

• It can identify visual defects such as layout issues, rendering problems, and functional bugs.

• Applitools provides a visual validation platform that integrates with various test automation frameworks and tools, making it easy to add visual testing to existing test suites.

Test Sigma:

• Test Sigma is an AI-driven test automation platform that aims to simplify test creation and maintenance.

• It uses natural language processing and AI to enable testers and non-technical users to create automated test scripts.

• The platform offers features like self-healing tests, easy maintenance, and cross-browser testing.

SauceLabs:

• Sauce Labs is a cloud-based platform for automated testing, including both web and mobile application testing.

•While it’s not exclusively an AI testing tool, it offers AI-powered testing capabilities to improve test execution and analysis.

•It provides a wide range of real devices and virtual environments for running tests and supports various testing frameworks.

TestCraft:

• TestCraft is a cloud-based test automation platform that uses AI to generate and maintain test scripts.

• Its AI engine identifies changes in the application under test and adjusts test scripts accordingly to ensure they remain functional.

• TestCraft supports web and mobile app testing and offers integration with various CI/CD tools.

These tools leverage AI and machine learning to enhance the efficiency and accuracy of quality assurance processes. When choosing the right tool for your specific needs, consider factors such as the type of applications you’re testing (web, mobile, desktop), the level of technical expertise in your team, and the integration capabilities with your existing testing framework or tools. It’s also essential to evaluate factors like cost, scalability, and support to determine the best fit for your organization’s requirements.

Conclusion

The integration of Artificial Intelligence into Quality Assurance is ushering in a new era of efficiency, accuracy, and effectiveness in software testing and product quality control. AI-driven solutions are revolutionizing the way businesses approach quality assurance, from automating repetitive and time-consuming testing tasks to enabling predictive defect identification and improving performance and security testing. With AI, organizations can streamline their testing processes, ensure consistent quality, and ultimately deliver higher-quality products to their customers. This transformation not only enhances product reliability but also reduces costs, accelerates time-to-market, and equips businesses with the agility needed to thrive in an increasingly competitive technological landscape. AI’s impact on quality assurance is a testament to the ongoing evolution of technology in the pursuit of excellence and customer satisfaction.

How can TestingXperts help transform QA using AI

TestingXperts, as a specialized software testing and quality assurance company, offers comprehensive AI-driven solutions to facilitate the transformation of your quality assurance processes. They can leverage AI to enhance the efficiency, accuracy, and agility of your testing initiatives. By employing AI-powered test automation, TestingXperts can help you reduce manual testing efforts, accelerate test execution, and enhance test coverage. Their expertise in developing self-healing test scripts and frameworks ensures that your tests remain robust even as your applications evolve, reducing maintenance overhead. Additionally, they can optimize test data management through AI, ensuring the availability of diverse and realistic data sets for thorough testing. This not only improves the quality of your tests but also contributes to data privacy compliance with AI-driven anonymization techniques.

Furthermore, TestingXperts can assist in predicting potential defects through AI-based analysis of historical data and patterns, allowing your QA teams to proactively address vulnerabilities before they impact your software’s performance. Their AI-driven performance testing solutions simulate real-world user behavior and load patterns, providing valuable insights into system performance under varying conditions. With their expertise in AI-enhanced security testing, TestingXperts can identify security vulnerabilities and weaknesses, helping you safeguard your applications and data. Whether you need to optimize test cases, automate testing processes, or bolster your security measures, TestingXperts’ AI-driven solutions are tailored to elevate the quality assurance landscape of your organization, enabling you to deliver robust, secure, and high-quality software products to your customers.

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Content
1. Top Nine Insurance Industry Trends To Watch Out for
 1.1 Distributed infrastructure
 1.2 Digital transformation
 1.3 Automation
 1.4 RPA
 1.5 Low/No Code Development
 1.6 Artificial intelligence (AI)
 1.7 Predictive analytics
 1.8 Connected insurance internet of things (IoT)
 1.9 Chatbots
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 2023 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 2023.

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 2023 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 2023, 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. According to Juniper, AI-based chatbots can enable cost savings of about $1.3 billion across life, property, and health insurance by 2023.

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 2023 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..

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Content
1. Quality assurance vs quality control
2. An overview of QA processes
3. An overview of QC processes
4. Quality assurance vs quality control: The similarities
5. Quality assurance vs quality control: Key differences
6. Conclusion
7. 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) and 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 we compare quality assurance vs quality control, both processes may have similar intentions but take different approaches to ensure customers receive a high-quality product.

Quality assurance vs quality control

Quality assurance:

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

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 are performed or an application is created, quality control can be termed as the ‘inspection’ aspect of quality management.

An overview of QA processes

QA involves activities and procedures performed during SDLC 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

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 assurance vs quality control: 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 helps ensure digital businesses that the finished product meets 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. QA and QC 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 adhering to QA and QC processes 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.

Quality assurance vs quality control: Key differences

Conclusion

Quality assurance is a combination of activities throughout the manufacturing process that ensures product quality. On the other hand, quality control is a set of procedures to ensure the product meets the quality requirements. Both are different from each other but, at the same time, essential when it comes to providing quality applications and services as they both lie at the core of quality management.

How can Tx help?

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 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.

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