1. Role of Technology in Modern Energy Grids
2. How does End-to-End (E2E) Testing Ensure Grid Reliability?
3. Key Components That Require E2E Testing
4. How Txs’ Quality Engineering Enhances Your Grid Reliability?
5. Conclusion

In the current tech-driven era, reliability is a primary factor in assuring product quality. As the energy grid faces several challenges from outdated software systems to increased demands, utilities are under pressure to ensure smart and uninterrupted services. Organizations dealing with energy and utility services know the cost of failure, i.e., disrupted lives due to outages, negative business impact, and economic strain. Yet, many software-related issues can be easily resolved with end-to-end (E2E) testing if higher authorities know that QA is the backbone of a resilient grid.

Role of Technology in Modern Energy Grids

Smart or modern energy grids comprise a network integrating energy distribution with digital communication. They allow utility enterprises to optimize energy generation and distribution, as customers benefit from what the data tells them (enabling them to understand energy usage). Right now, AI is becoming the key factor in running smart grids. AI agents are helping stabilize energy networks and reduce financial risks related to infrastructure instability. Some of the AI and ML applications in the smart grid involve:

• Better Agility and Resilience: Sensors and automation help identify vulnerable grid parts and respond with automated rerouting. This helps store backup energy during peak generation times, which can be rerouted when there’s a gap in the flow. 

• Improved Security: Cybersecurity is one of the major concerns across all businesses. Seeing the increasing number and complexity of cyberattacks, AI tools will help address the risk factors by identifying network attack features, intrusion, and malware. 

• Enhanced Automated Switching: AI tools can predict grid imbalances, which will soon allow utility enterprises to switch protocols from manual to automated. This will enable utility organizations to reroute energy before damage or outages expand to other areas. 

• Flexible DSM: When energy demand reaches a peak value, it greatly strains utility enterprises. AI and smart meters will help schedule, plan, and monitor energy demand fluctuations to ensure companies can fulfill them. 

How does End-to-End (E2E) Testing Ensure Grid Reliability?

The rapid increase in the energy sector’s dependency on next-gen technologies and digital systems highlights the necessity of rigorous software testing. From real-time monitoring via IoT systems to predicting weather patterns with AI, this sector is heavily utilizing digital software. Thus, ensuring such software and devices’ reliability, efficiency, and security becomes highly important. Given that electricity consumption might double by 2050, the digital transformation will help ensure the smooth shift to renewable energy by ensuring service speed, scalability, and stability. 

As the energy and utility (E&U) sector reaches core transformation, end-to-end (E2E) testing will play a significant role here. Let’s take a look at how E2E testing will ensure smart grid reliability for utility companies:

• Validating E2E System Interactions: A grid is a complex architecture involving SCADA systems, OMS platforms, DERs, customer apps, and smart meters. End-to-end testing ensures the seamless working of these components by simulating real-world scenarios, testing data flow across systems, and catching integration issues missed by unit or component testing. This helps prevent chain-reaction failures and ensures consistent operations.

• Verifying Real-time Data Accuracy and Responsiveness: Grid operators depend on real-time data to make critical decisions. E2E testing confirms that telemetry data from field devices is correctly captured, transmitted, and processed. It also ensures that the control commands like load shedding, DER dispatch, etc., are executed with minimal latency. This is crucial for maintaining frequency stability, voltage levels, and system balance.

• Ensuring Reliability During Updates: Modern energy systems regularly undergo software and firmware updates. With end-to-end testing, utility enterprises can ensure that the new updates do not break existing functionality and cause downtime. It also ensures proper working of rollback procedures and allows continuous improvement without compromising reliability.

• Supporting Resiliency with Disaster Recovery and Failover Testing: E2E testing simulates critical failure scenarios to validate automatic failover between redundant systems, data recovery mechanisms after a cyberattack or system outage, and restoration workflows in outage management systems (OMS). This ensures the grid can recover quickly and predictably from disruptions.

• Validating Security Across the Entire Tech Stack: With increasing cyber threats, end-to-end testing includes security checks such as authentication and authorization validation, encrypted communication paths across SCADA, DER, and cloud services, and intrusion detection response flows. This helps mitigate the risk of breaches that could destabilize the grid.

Key Components That Require E2E Testing

• Supervisory Control and Data Acquisition Systems: SCADA systems are critical for real-time grid monitoring and control. With end-to-end testing, energy and utility organizations can ensure telemetry accuracy from field devices, real-time command execution and feedback loops, feasible alarm handling, and visualization integrity. Testing SCADA’s integration with other subsystems like DER and OMS is crucial to prevent false alarms or delayed responses.

• Outage Management Systems: OMS platforms manage fault detection, outage restoration, and customer notifications. E2E testing validates correct fault location from incoming SCADA or AMI data, automated dispatch workflows, and real-time updates to support teams and customer portals. It helps minimize outage duration and ensure regulatory compliance for restoration timelines.

• Distributed Energy Resources: DERs like rooftop solar, EVs, and battery storage support bidirectional energy flow. E2E testing ensures seamless integration of DERs with the control center and forecasting tools. It also facilitates proper communication and coordination during grid events. 

• Smart Meters and IoT Devices: Smart meters collect data on usage, outages, and power quality. Enterprises can implement E2E testing to assess data transmission quality to data concentrators and analytics platforms. It will ensure secure firmware updates and configuration management and facilitate the correct link between meter data and customer accounts.

• Customer Portals and Mobile Apps: These platforms must accurately reflect real-time and historical data. With end-to-end testing, enterprises can enhance outage reporting and restoration timelines, streamline billing history and usage analytics, and manage alerts and program enrollment.

• Billing and Data Analytics Platforms: These revenue-critical and operational platforms must be tested for accurate data integration from multiple sources (SCADA, AMI, DER). E2E tests also check integration with CRM and financial systems and evaluate performance under different data loads.

How Txs’ Quality Engineering Enhances Your Grid Reliability?

Digital technologies are helping utility organizations improve energy efficiency and diligently address climate challenges. However, these technologies face a critical issue of unreliability and unpredictability, which affects the energy sector’s robustness. As the energy and utility sector undergoes a massive transformation, testing the digital solutions becomes inevitable. End-to-end quality engineering services for the E&U domain ensure the desired outcomes of reliability, security, and scalability, supporting change management, monitoring, and compliance programs. Our testing services for E&U sector covers:

• Automation-Driven Regression Testing: As grid software requires frequent updates in AI models, device firmware, and customer-facing portals, building an automated regression suite becomes essential. We ensure that the existing functionality of your grid software remains intact with every release and reduces manual testing effort across systems.

• Performance and Scalability Testing: Smart grids must handle millions of real-time data transactions from distributed sensors and customer devices. We perform load and stress testing to validate your system behavior under peak usage, ensuring no degradation in data processing, user access, or system responsiveness.

• Data Integrity and Consistency Validation: Test data pipelines across SCADA, OMS, DER, and billing systems to ensure data consistency and correctness. We introduce validation checks to detect data loss, duplication, or mismatches between source and destination systems, ensuring accurate billing and customer communication.

• Test Data Management and Simulation: Develop synthetic data models simulating real-world scenarios such as grid failures, cyber-attacks, or energy demand spikes. We enable meaningful E2E test execution without impacting production data while ensuring realistic coverage.

• Device and Firmware Compatibility Testing: We help you validate the compatibility of various smart meters, sensors, and IoT devices across different firmware versions to ensure stable integration with the central platform. This reduces field issues caused by device-software mismatches.

• AI/ML Model Validation: When AI models predict grid failures or optimize energy rerouting, we help you validate their accuracy, bias, and performance against historical data and real-world events. This reduces the risk of faulty predictions impacting grid operations.

• Security Penetration and Vulnerability Testing: We go beyond functional testing by integrating penetration testing and vulnerability scanning as part of E2E testing. This helps uncover security gaps like exposed APIs, unsecured data transmissions, or unauthorized access paths across the grid’s tech stack.

• Compliance and Regulatory Verification: We validate that all your customer-facing applications, data handling processes, and operational systems comply with energy sector regulations such as NERC CIP, GDPR, and regional energy mandates. This reduces legal and operational risks.

Partnering with Tx for end-to-end testing ensures efficient handling of stress and load on your applications in multiple locations. This would result in higher credibility and trust among your target customers. Investing in quality engineering services will also help you uncover security vulnerabilities to protect yourself from potential cyberattacks. Txs’ tailored software testing solutions for the energy, utility, and oil & gas industry help organizations improve business processes, quality, energy efficiency, and customer satisfaction.

Conclusion

End-to-end testing helps energy providers deliver reliable and secure services by checking whether all systems, from smart meters to outage management, work well together. It helps organizations ensure real-time data accuracy, smooth software updates, quick failure recovery, and strong security. As grids adopt more advanced tech like AI and ML, testing becomes even more important to avoid service disruptions and protect against cyber threats. By partnering with Tx, a leading QE expert, you can improve performance, reduce downtime, and build customer trust while moving toward a smarter, more stable energy future. To learn how Tx can assist you, contact our E2E QE experts today.

The post How End-to-End Testing Supports Grid Reliability for Energy Providers first appeared on TestingXperts.

1. Quality Engineering Trends for 2025
2. AI and Machine Learning in Quality Engineering
3. Integrating Big Data with QE
4. Shifting to Cloud
5. DevOps in Quality Engineering
6. Adopting a Security-First Testing Approach
7. Conclusion
8. Why Partner with TestingXperts for Quality Engineering Services?

Imagine a digital e­nvironment where e­fficiency, accuracy, and innovation come togethe­r to create a seamle­ss and flawless user expe­rience. Various trends are­ helping businesses stay ahead in the constantly changing software deve­lopment and testing field. Tools like­ AI-powered testing and smarte­r automation techniques are assisting companie­s, and Quality Engineering is among those trends. The World Quality Report from Capgemini Group, Soge­ti, and Micro Focus states that QE is necessary for impleme­nting emerging technologie­s, as 85% of organizations acknowledge its importance for re­al-world applications. This fact is further backed up by the rise­ of sustainable IT and the integration of cutting-e­dge technologies like­ Blockchain and the Metaverse­, signaling an apparent movement towards sustainable­ and efficient IT practices.

Quality engine­ering is steadily evolving due­ to artificial intelligence and machine­ learning. These smart te­chnologies are enhancing typical te­sting methods while prese­nting innovative practices. For example, se­curity testing is adapting to tackle the widespread cyber threat risk in our highly digital and linke­d world. Furthermore, predictive­ quality engineering utilize­s data analytics to foresee­ and handle potential issues.

Moving forward­, let’s look at the­ six most significant software quality enginee­ring trends anticipated for 2025. These­ emerging patterns will aid organizations in re­maining competitive in the global marke­tplace.

Quality Engineering Trends for 2025

AI and Machine Learning in Quality Engineering

Artificial Intellige­nce (AI) and Machine Learning (ML) change­d how software quality is ensured in today’s te­chnology world. AI and ML tools can do quality checks better and faste­r. They can look at lots of information, see what conne­cts, and find problems early on in the development. Integrating both with quality engineering can help organizations in following ways:

• With AI-enabled test automation, tedious and time-consuming tests can be­ done by tools. This lets quality engineers focus on harder and more­ creative testing tasks.

• Machine­ learning programs can learn constantly from test re­sults. They make test things be­tter and testing more precise. This helpful integration betwe­en AI, ML, and quality enginee­ring makes testing quicker and improve­s how reliable and robust software products are­.

• AI and machine le­arning are crucial in predictive­ maintenance and bug dete­ction in quality engineering. By using past data and curre­nt monitoring, these technologie­s can tell when a system or part will probably fail. This allows re­pairs before problems happe­n and less downtime.

• Quality engine­ers can use predictive­ analysis to find possible weak areas in the­ development proce­ss and fix them before the­y become serious issue­s. It reduces de­fects after rele­ase and helps make the­ user experie­nce better. This makes sure software and products mee­t very high quality and reliability standards.

Integrating Big Data with QE

In 2025, vast amounts of information from data and IoT devices will strongly affect how companie­s ensure software quality. Ne­w technologies are changing how businesses create software­ and test products. They’re he­lping quality engineering work be­tter and more precisely. Big data gives a lot of de­tails to help understand patterns and what happe­ns over time. Information like how people use things, system facts, and performance figures feed the datasets. Ways an organization can benefit from quality e­ngineering combined with big data include:

• Quality engineers use lots of information to see patterns or odd things. Based on a deep study of information helps with better choice­s and finding problems or ways to improve.

• Advanced analytics can pre­dict quality issues before the­y happen. Knowing about problems early enables companies to stop them, which saves time­ and money. This way of working early me­ans fewer bug fixes is ne­eded after making products.

• Big data analytics help make­ testing plans better by highlighting essential things to pay attention to and knowing where to focus testing make­s it more useful.

Shifting to Cloud

The use­ of cloud technology has become quite­ common in today’s tech world. There­fore, it is crucial to incorporate quality e­ngineering practices during application update­s. Many businesses are progre­ssively transitioning to cloud-based platforms and digital software-as-a-se­rvice solutions. However, e­stablishing such systems can be an intricate proce­ss, especially for larger proje­cts. Developing a cloud infrastructure ne­cessitates care in the­ crucial phase of configuration. Errors may arise, like issue­s with hardware or storage configurations. Such mistakes have­ the potential to induce syste­m faults or even cause the­ system to cease functioning e­ntirely. Thus, technology leade­rs require partners that offer thorough testing solutions.

DevOps in Quality Engineering

The inte­gration of DevOps and Quality Engineering in the­ year 2025 represe­nts a fundamental change in software deve­lopment and deployment strate­gies. Speed, agility, and high quality are essential in today’s challenging software development market. Through DevOps, where­ software developme­nt and IT operations collaborate closely, the­ software lifecycle is shorte­ned while guarantee­ing high-quality deliverables. As a component of DevOps, quality engine­ering will move quality testing e­arlier in the process to furthe­r enhance outputs.

Integrating continuous inte­gration and continuous testing are crucial facets of the­ modern DevOps and quality engine­ering relationship. They will promptly pinpoint problems and guarantee new changes do not degrade software quality. This re­sults in a smoother developme­nt process with fewer e­rrors and faster rollouts.

Adopting a Security-First Testing Approach

Ensuring software syste­ms are robust, resilient, and se­cure is crucial as cyber risks continue changing. Adopting a systematic approach focused first on security involves thoughtfully conside­ring protection measures from the­ beginning of development to the final de­ployment. Designing with defe­nses in mind from the outset allows proactive­ identification and resolution of possible we­aknesses, safeguarding against a varie­ty of cyber dangers. Careful atte­ntion to security at every ste­p of building software helps organizations stay ahe­ad of evolving threats.

When utilizing a se­curity-first testing methodology, security te­sting is smoothly incorporated into each phase of de­velopment, guarantee­ing that security measures are­ not merely an addon but rathe­r an innate part of the software’s core. This methodology include­s rigorous screening for typical security susce­ptibilities like injection assaults, cross-site­ scripting, and data breaches. Automated se­curity testing tools are essential in productive­ly examining code for potential flaws, enabling quicker identification and resolution of security issues.

DevOps and Shift-Left Testing Integration

Implementing DevOps and Shift-Left Testing as a transformational strategy has become a popular approach in modern software development, highlighting collaboration and efficiency in the development process. DevOps allows for practical cooperation between development and operations teams and is complemented by Shift-left Testing, a process essentially enabled by early testing. Collectively, they allow the delivery of the best-in-class software while maintaining an active and robust testing matrix.

Integrating shift-left testing and DevOps results in early detection and rectification of problems arising, thereby blocking the defects from continuing downstream in the development. With automated testing, developers can quickly and continuously get code feedback, allowing them to fix any emerging issues and thus create other benefits, including enhancing software quality and decreasing test cycles. By adopting this integrated approach, companies can accomplish faster release cycles, better team collaboration, and more reliable products that meet customers’ needs.

Conclusion

Investing in quality engineering is key to success in 2025’s fast-paced digital world. Artificial Intelligence and Machine Learning integration are changing how businesses ensure quality, offering pinpoint accuracy and speed. The move towards using cloud-based data shows the need for data solutions that can grow and work efficiently, especially in our data-driven world. The combination of DevOps and quality engineering, along with considering security as a first thing, marks a big change in how businesses develop and market software. All these changes suggest a future where quality engineering uses data more, plans, and is more involved than ever. Organizations that make these changes are setting new standards in quality, speed, and how they perform, making sure they give top-quality products and services in a world that’s more and more digital. The path of quality engineering in 2025 and later is about constant innovation and flexibility, shaping the future of tech and business.

Why Partner with TestingXperts for Quality Engineering Services?

In the evolving domain of software development and testing, selecting the right partner for quality engineering solutions is crucial. TestingXperts is a leading QE provider for companies aiming to upgrade their quality engineering levels. This recognition doesn’t just stem from our services but from how we present those services with unparalleled skill and innovation.

• Our QE team is an AI, ML, and cloud technologies expert, providing best-in-the-industry quality services to help you reach your business goals.

• Every client has different requirements and objectives. Whether it’s about working with your current software or adding new testing methods, we make sure you get quality services based on your needs.

• Security can’t be ignored in our high-tech world. TestingXperts ensures your software is in line with all the necessary regulations and quality levels.

• Our QE experts use the latest tools and in-house accelerators, from automated software to cloud-based environments, to improve your work quality and speed. It helps in improving time-to-market for your products without lowering quality.

To know more, contact our software quality engineering experts now.

The post Top 6 Quality Engineering Trends for 2025 to Stay Ahead of the Curve 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.

According to statistics, in 2025, 40% of the company’s IT budget will be spent on AI testing applications. As digital innovation accelerates, quality engineering (QE) tools and AI are upscaling the process’s accuracy and establishing new product/service excellence standards. In the rapidly changing digital space, it has become a necessity to incorporate AI in quality engineering to turn data into valuable insights and automate tasks that require extensive manual effort.

Research shows that 80% of software terms will use AI in 2025 in one way or the other. So, if someone is not using AI in their professional or personal life, it will change now. AI is becoming necessary for QE leaders to remain competitive and embed it into every aspect of SDLC, business operations, and customer experiences. So, in which direction is quality engineering going?

The AI Wave in Quality Engineering

In Big Data, AI algorithms analyze massive datasets like past defects, code changes, and requirements to identify patterns and shortlist potential issues. This enables QA engineers to focus on priority areas and improve the reliability and accuracy of software testing. AI-based tools optimize test result analysis, allowing engineers to achieve comprehensive QA coverage.

In cloud computing, QE is critical because of the unique characteristics of cloud-based applications. These apps should be reliable to maintain uninterrupted service and minimize downtime issues. AI upscales cloud app testing by automating test case generation and execution, reducing human errors. Also, AI can replicate user behavior and patterns to run large-scale performance testing under varying scenarios.

Integrating IoT and AI opens the door to advanced quality engineering activities. Smart gadgets, with their embedded devices, increase the complexity of the IoT ecosystem. Quality engineering helps establish QA environments to test embedded software across multiple devices. AI optimizes this process by facilitating more accurate and faster testing to ensure firmware and components work in sync while improving product quality.

AI Trends Reshaping Quality Engineering

Turning Fear into Excitement

Do you remember the feeling you get when riding crazy rides in the amusement park for the first time? At that time, your initial fear about the crazy rides quickly turned into excitement. That’s how AI is making everyone feel around it. After years of uncertainties about the AI trust factor or whether it will take our jobs, it is starting to clear up. According to the US Bureau of Labor Statistics, software development and testing jobs will grow much faster compared to the average of all occupations from 2023 to 2033. They have credited AI for driving this change, as enterprises notice positive results by investing in AI. QE teams are also implementing AI to identify testing needs based on requirements, generate test cases more quickly, and implement self-healing capabilities to maintain test cases.

Generative AI Adoption

GenAI is speeding up test case generation by automatically creating tests and analyzing user stories using NLP. It allows quality engineers to generate test cases within seconds and deliver expected test results. By prioritizing relevant test cases based on the risk factors, QE teams will shorten the test cycle time and cut costs while minimizing production errors.

AI Agents and Humans Working Side-by-Side

If we talk about what’s trending in the tech industry for 2025, then Agentic AI comes first. It will be the front-runner of the AI tool collection. This technology will allow quality engineers to automate the testing steps in the SDLC based on business objectives and context. It would allow them to write and review code for errors and take away the tiresome task of bug fixing so that they can focus on business-critical processes.

Generative AI Transforming Test Automation

1. 1. It dynamically analyzes application behavior to generate test cases, reducing manual involvement and ensuring broader test coverage.
   2. Traditional test scripts are more likely to break when the UI changes. GenAI facilitates self-healing test scripts that adapt to code changes, minimizing maintenance costs.
   3. AI-driven pattern recognition identifies anomalies faster, predicting potential failures before they impact users.
   4. Teams generate and execute test scripts using simple language prompts, making testing more accessible for non-technical users.
   5. GenAI creates diverse, high-quality test data, enhancing testing efficiency and improving application reliability.

Overcoming AI Implementation Challenges in QE

1. 1. 1. Introducing new technologies can be complex and hinder the successful adoption of AI solutions. One must have adequate AI expertise to ensure the right selection, adoption, and usage of tools and allocate a budget to put them to work.
      2. Dev and quality engineering teams may resist the change and prefer a conservative workflow approach. The leadership team would be responsible for convincing the workforce of the necessity and benefits of using AI-based solutions.
      3. Another challenge is employee training and adaptation. To enable teams to get used to using AI tools, businesses need to invest time and budget to train their employees and help them adapt to changes.
      4. There can be concerns about bias or inadequate results of AI-based models because of the injected low-quality information. It would result in gaps in test coverage or an inaccurate defect identification.
      5. Using AI-based tools in process automation often involves handling personal/sensitive information, increasing the risk of data leakage. Businesses must implement proper security measures to prevent unethical access to information.

Why Partner with Tx for Quality Engineering Services?

1. 1. 1. 1. E2E product testing
         2. Functional testing
         3. Test automation
         4. Web & mobile testing
         5. Performance testing
         6. Security testing
         7. Usability testing
         8. Accessibility testing
         9. Data testing & analytics
         10. Data management

Summary

AI is upscaling quality engineering by enhancing testing precision, streamlining processes, and ensuring robust software performance. From big data analytics to cloud computing and IoT, AI-powered tools are enabling faster defect detection, optimizing test execution, and improving product reliability. Generative AI further advances test automation by generating test cases, enabling self-healing scripts, and predicting failures. However, AI adoption has challenges like expertise gaps, resistance to change, and security concerns. Tx’s quality engineering services leverage advanced automation tools to enhance test coverage, efficiency, and accuracy while helping you overcome implementation hurdles through strategic planning and workforce training. To know how Tx can help, contact our AI experts now.

The post How AI is Transforming Quality Engineering in 2025 first appeared on TestingXperts.

1. Data Engineering in Retail
2. Benefits of Data Engineering in the Retail Industry
3. Data Engineering Use Cases in Retail Industry
4. How can Tx help with Data Engineering in Retail?
5. Summary

With high competition and rising consumer expectations, retailers always seek new tech solutions to improve their operational efficiency and CX in the retail industry. But the question is, “How can they keep up with these demands and remain competitive?” The answer is “data engineering.” As businesses require better data analytics systems, they need new ways to facilitate access to high-quality information for data scientists, analysts, and other stakeholders. Data engineering offers actionable insights to support strategic decisions, implement innovative tech solutions (AI, RPA, etc.), and optimize operations to improve business profit margins.

According to McKinsey & Company, organizations using data and digital technologies benefit significantly from decision-making and operational performance. It’s not just about handling data but rather about converting it into an asset supporting informed decision-making and better business outcomes. The best way to achieve this is by utilizing big data and behavior retail analytics, making the best plans and decisions, understanding customer requirements, uncovering market trends, and more.

Data Engineering in Retail

Data engineering is about designing and building systems to collect, store, and analyze data at scale. Retailers can collect huge amounts of data and assign the right technology and people to ensure its usability when it reaches data analysts and scientists. Implementing data engineering in the retail sector involves systematic data collection, storage, and processing to ensure it is accessible, reliable, and available for analysis. During the process, the data engineer performs the following tasks when working with data:

• Acquire datasets aligning with business requirements

• Support data streaming systems development

• Leveraging new systems for data analytics and BI operations

• Develop BI reports and algorithms to transform data into actionable and useful insights

• Build, test, and streamline database pipeline system

• Build new data validation methods and analytics tools

• Ensure compliance with security protocols and data governance policies

Benefits of Data Engineering in the Retail Industry

Almost 62% of retailers say using information and analytics gives them a competitive advantage, compared to 63% of cross-industry leaders. To better understand the role of data engineering in the retail industry, let’s take a look at the following factors that allow retailers to unlock significant efficiencies in their operations:

Data Collection and Integration:

Data engineering helps collect and integrate data from diverse sources, such as POS systems, online transactions, supply chain operations, and user feedback. It ensures that information is seamlessly integrated and offers a holistic view of business operations. Data integration allows for more accurate inventory management, personalized marketing strategies, and demand forecasting.

Data Storage and Management:

After collecting data, it must be stored securely and organized to facilitate retrieval and analysis. Retailers use data warehousing and cloud storage solutions for this purpose. These systems handle large-scale data operations, allowing retailers to adapt to changing business needs.

Data Processing and Analysis:

Data processing and analysis are at the core of data engineering, extracting valuable insights. Retailers deploy advanced analytics, ML algorithms, and real-time processing to decipher their data. This reveals patterns in customer behavior, operational bottlenecks, and cost-saving opportunities.

Automating Operations:

Data engineering facilitates the automation of routine tasks. This reduces the time and resources needed for manual data entry, report generation, customer service, etc. Retailers can allocate more resources to strategic tasks and innovation.

Enhancing CX:

Data engineering plays a crucial role in improving CX. Sophisticated data analysis can make personalized shopping experiences, optimized customer service, and targeted marketing campaigns possible. Retailers can design their services by understanding customer preferences and behaviors to meet specific needs, improving loyalty and satisfaction.

Data Engineering Use Cases in Retail Industry

To understand the value of data engineering in the retail industry, let’s take a look at the top five use cases, which are currently being implemented by leading companies:

Customer Behavior Analysis:

Data-driven customer insights are important to resolve retail challenges, such as personalizing campaigns to improve revenue, optimizing customer conversion rates, lowering acquisition costs, and avoiding customer churn. These days, users interact with a brand through mobile devices, stores, eCommerce sites, social media, and more. This increases the complexity of the data type and the variety of retailers that have to categorize and analyze it. Data engineering helps unlock insights from customer behavior data (structured and unstructured), enabling retailers to combine, integrate, and analyze data to facilitate customer acquisition and loyalty.

Personalizing In-Store Experience:

Before big data was launched, merchandising was considered an art form. Retailers did not have any means to measure the impact of their merchandising decisions. Later, a new trend was introduced when big data and online sales grew. Shoppers would physically look for the products in-store and order them online later. This gave rise to tracking technology which offers new means to analyze store behavior and measure merchandising impact. Data engineering allows retailers to drive meaningful data to optimize their merchandising strategies, personalize the in-store experience, and offer discounts to encourage users to complete purchases. The end goal is to improve sales rates across all channels.

Improving Conversion Rates:

A 360° view of customers’ buying habits and prospects can help increase acquisition and lower costs. Retailers can then effectively target promotional campaigns. Earlier, customer data was limited to demographic data collection means. But today, user interaction is more due to the influence of social media and other channels. Data engineering allows retailers to correlate customer purchase histories with their behavior on social media sites. It often reveals unexpected insights that retailers can use to target their ads by placing them on multiple channels like Facebook pages, Insta Ads, TV shows, etc. They can test and measure the impact of their promotional strategies on conversion rates. Data engineering also helps identify user interests and generate personalized promotions.

Customer Journey Analytics:

Today, customers are more connected than ever because of smartphones, eCommerce, and social media. They can access information and decide what, where, and when to buy and at what price. Customers can make better buying decisions and purchase wherever and whenever they want. Due to this, marketers have to continuously analyze, understand, and connect with customers. It requires a data-driven approach to understand the customer’s journey across channels. Data engineering helps analyze structured and unstructured data, regardless of the type. This reveals patterns and insights about what’s happening in the customer journey, who are high-value customers, and the best approach to approach them.

Supply Chain and Operational Analytics:

Retailers face intense pressure to optimize their performance, service quality, budgets, and asset utilization. This will eventually help them gain a competitive edge and improve business performance. The key is using data engineering platforms to improve operational efficiency and unlock insights locked in machine, log, and supply chain data. It combines structure data from ERP, CRM, geolocation, public data, and mainframe and syncs them with unstructured data.

How can Tx help with Data Engineering in Retail?

With its data engineering and quality assurance expertise, Tx is crucial in optimizing your retail operations. Our data testing services ensure your retail data is accurately processed and ready for analysis. Partnering with Tx would give you the following benefits:

• We have deep expertise in analytics testing, data warehousing, and big data testing engagements.

• We assist retailers in harnessing the full potential of AI/ML and predictive analytics to understand and decipher customer behavior and market trends.

• Automation is the key to business success. Our test automation experts utilize advanced in-house accelerators, such as Tx-HyperAutomate, Tx-SmarTest, etc., to help you integrate automation with routine tasks like data entry, report generation, and resource allocation.

• Our customized data testing approach ensures data accuracy at various levels of data engineering projects.

• By partnering with Tx, you can transform your data into a strategic asset to drive informed decision-making, optimize operational efficiency, and improve CX.

Summary

Data engineering is necessary for retailers to stay competitive, meet consumer expectations, and enhance CX. It involves handling data strategically from the acquisition and integration to analysis and practical application. The process enables retailers to make better business decisions, optimize supply chain operations, and implement innovative solutions like AI, RPA, etc. This leads to reduced operational costs, better decision-making, and improved CX. Data engineering supports advanced predictive analytics and ML to pave the way for robust fraud detection and pricing strategies. However, one must partner with an experienced data engineering specialist like Tx to ensure its successful integration with retail operations.

To know how we can help, contact our experts now.

The post How does Data Engineering in Retail Maximize Efficiency? first appeared on TestingXperts.

Why Quality Engineering?

In recent times, the process of software development has moved towards agile and DevOps processes as businesses are in need for faster releases to get to market faster. Evidently, with agile and DevOps processes on the go, the software testing process has taken a shift-left approach wherein the testing is done parallel to software development to ensure faster identification of bugs and quality releases.

The QA in the agile process tests the entire system based on the sprints to cater to software features development.The QA teams take up software testing along with the development process, from the initial stages of DevOps process to ensure continuous integration(CI) and continuous delivery(CD) to ensure faster releases.

The DevOps CI/CD and agile development processes have changed the world of quality assurance. The QA teams have less planning time due to continuous releases during sprints and hence they struggle to accomplish regression testing on time.

Today, the QA teams are required to think more creatively and realistically than ever before. Thus, the QA has taken a shift from Quality Assurance to Quality engineering, to ensure faster releases enforced with DevOps CI/CD and agile processes.

Software quality engineering (SQE) assures high standards during a software development process and specifically holds good for agile and DevOps methodologies. Even in Quality Engineering, the key role is played by the quality engineers who create, implement and maintain systems to ensure quality processes.

Why Quality Engineering role evolved?

1. This change from QA to QE has been evident with more stress given upon automation and automated testing solutions being in place

2. With the connected and complex systems and APIs in place, testing the backend services has become complex as there is no visible UI which led to Quality Engineering emergence.

3. Significantly testing moved differently from unit, smoke tests towards integration, performance, usability and security testing, requiring the QA to be involved much earlier in the software testing process.

4. Today, with the products being continuously evolved as smart products and apps, QA teams don’t always have weeks to analyze requirements and create test cases in isolation and they have to think smartly on the go and rapidly develop test plans as the products evolve

Why your enterprise should transform from Quality Assurance to Quality Engineering?

1. QA team in close association with the developers and the operations (DevOps) teams ensure faster and continuous releases by enforcing test automation initiatives and shifting QA efforts towards quality assurance by distancing silos.

2. QA teams follow a shift-left approach of testing to find defects early and speed up the time to market. The shift left allows developers to fix defects more quickly and at much lower cost, speeding time to market and increasing customer satisfaction.

3. QA is involved today to think like end-users and ensure to deliver great customer experience irrespective of the industry they operate especially while testing mobile applications.

4. With the increasing complexity of mobile apps and with IoT devices in place, testers should be more informed with various real-time scenarios and should be able to find real-world defects effectively.

5. In today’s digital age, QA teams need to concentrate more on probable security risks that might arise when data is shared among systems, rather than focusing only on the security requirements of the standalone applications.

6. QA teams are also involved to implement new tools and various automated processes much early in the development lifecycle to ensure faster releases with test automation and even use test automation frameworks to achieve faster releases to deliver quality products.

All the above stated have culminated into a shift from Quality Assurance to Quality Engineering and this shift ensures integrating quality into the entire SDLC to engineer better quality. Further, this transformation has also led to a new role of SDET (Software Developer in Test) responsible to build test infrastructure, identify test scope, and define quality criteria. These SDET’s start with unit testing and extend to all other tests and should even build a mitigation strategy in case the situation demands.

They are responsible to manage and maintain CI/CD pipelines to ensure the code keeps moving through the pipeline. They work closely with all teams of developers, operations teams and the product owners. They should also have a deep eye on what should be automated and should be able to plan quickly and efficiently based on the application under test.

As a final thought, though there has been a broader role given to the QA in the agile and DevOps processes with a move towards Quality Engineering, but still they are responsible for analyzing and validating requirements, developing and executing test cases placing effective efforts on automation qa and assisting in the resolution of bugs.

Software quality assurance by the QA teams plays a key role in the SDLC to enable a quality product. Earlier, as the products and applications were relatively simpler in their outlook, the QA teams tested the products and ensured quality is maintained. But, today with agile and DevOps processes on the go and with the evolution of smarter and complex products, there has been a need for the QA automation services to evolve as QE to support smarter and quality testing. This transition from Quality Assurance to Quality Engineering assures faster releases with DevOps CI/CD processes and ensure quality is delivered to end-users.

Talk to our industry experts for more queries on QA to QE transformation.

The post Why Should Enterprises Focus on QA to QE Transformation? first appeared on TestingXperts.

2. The Big Burst – Choices, Challenges, and Complexities

3. Why QA needs an intervention?

4. But – How to evolve from QA to QE?

5. QE – More than a Vowel Change

How to Evolve from Quality Assurance to Quality Engineering

The report unravels how ‘user satisfaction’ is coming on the top of the stack of most Quality Assurance (QA) and testing strategies. The advent of customer-centred innovation, digital transformations, agile and DevOps approaches, Internet of Things (IoT), Cloud, etc. is fuelling this change. But above everything else, what stands out is a list of recommendations that make you reconsider the very role of QA.

The report urges professionals and managers to:

– Amplify the level of smart test automation

– Transform QA and test function to support agile development and DevOps teams

– Invest in smart QA and Test platforms, and

– Define a test platform strategy and QA strategy on an enterprise level

But these are no surprises. When the report noted that 99 percent of respondents are using DevOps in at least one of their projects and that automation is emerging as the biggest bottleneck that is holding back QA and testing today

– is it not just iterating what you see in your office and scrum-huddles every now and then?

The Big Burst – Choices, Challenges, and Complexities

We are in a time where devices are proliferating at a never-before speed, where 97 percent of respondents (in the report) show some kind of IoT presence in their products and where Cloud, DevOps, and Agile have ceased to be mere power point enhancers. They are now stark realities that software and application teams encounter and leverage every day.

No wonder, there is a pressing need to bring Software Development/Design Engineer in Test (SDETs) into the team; and to inject skills in security, non functional testing, test environments, and test data management among testers. The challenges and context of this modern software/applications world have created a seismic shake. It is a post-Uber planet where only the best will survive. And relying on QA alone is not going to make the cut here.

Why QA needs an intervention?

Quality Assurance (QA) only entails activities for ensuring quality, spotting flaws early enough, code review, analysis, and refactoring. But the users need a stronger and broader variant of this approach. One that goes beyond testing cycles, one that percolates into the culture and the very way developers and designers think about software. Yes, get set for the arrival of Quality Engineering (QE).

Quality Engineering transcends quality control, quality assurance, and testing. It is proactive, strategic, forward-looking, intuitive and is way bigger in scope than QA. It is not limited to processes and procedures. It expands into the realm of the way these processes come up – right at the nucleus of ideation and user-empathy. It straddles across all areas of QA and testing and lifts quality to an altogether new level.

You will notice that QA is inclined towards some earlier stages of the software development, and had a postcode-writing role wherein QA teams checked what developers had written. But QE is not the fag end of a software cycle. It is a radical way that starts way ahead of where code begins. It permeates the entire development flow. QE helps organizations and developer-tester teams to come together against the onslaught of the diversity as well as the exponential rise of too many devices, platforms, applications, and content needs.

QA alone would not suffice to match the speed, persistence, and thoroughness that the Agile development and Shift-Left world demands today. Quality Engineering ensures that quality is embraced early on and is enhanced at every step and desk – and not just at the exit door doormat of software code. It undertakes end-to-end and architectural approaches for comprehensive software quality.

But – How to evolve from QA to QE?

Organizations have to embrace this new culture and mindset to embark on this massive shift.

This is where a continuous integration model between developers and testers would come into play. Testing becomes consistent, embedded into code design and gets easy to integrate into the entire chain. Development becomes iterative, collaborative and adaptive. It also entails localization of problems and fixing of individual parts so that all red flags are addressed before the whole software adds up. Organizations will have to usher in a new way of looking at and designing the software development lifecycle. This has to be fortified with sustainable test automation frameworks and methodologies, as well as Continuous Integration endeavours.

Resources and bandwidth would have to be furnished so that a test infrastructure can flourish and integrate without any scalability or latency hiccups. Test environments and production environments would also need to be conjoined in a way that quality becomes a precursor rather than an afterthought. Automation of tests might be called for. QA folks might be asked to think and code like a user.

This is where the culture and habit parts would face a makeover. Helping and empowering others – beyond organizational silos, dependencies and software hierarchies – will be the new norm for Quality Engineering to get into action. Even the delivery aspects would change- get set for a scenario of multiple releases and entire-system-checks. QE teams are involved and impactful in software design at a new degree and depth; so that core functionality tests can be planned for with a proactive edge.

In short, the move to QE reflects but is not limited to, the switch-over from waterfall era to Shift-Left era of software. With QE in place, quality travels right up to the north-most point of any software.

QE is about thinking of quality all the time, at every level and by everyone around.

QE – More than a Vowel Change

Businesses in the current era of impatient customers cannot afford to have even a small disruption in their business-uptime. That gives a different gravity to quality. That is where QE shines. And a QE professional is much more than a coder or a tester.

Project managers and CTOs are gearing up to tap this new face and fuel of quality. It’s time for a new regimen – one that does more than simply measure.

The post Evolving from Quality Assurance to Quality Engineering first appeared on TestingXperts.