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Tag: Quality Engineering

QA Automation Frameworks for Enterprise Applications
Modern enterprise applications demand testing strategies that can keep pace with continuous delivery, distributed architectures, and increasingly complex deployment pipelines. Traditional manual testing approaches often become a bottleneck as engineering teams scale their applications and release software more frequently. This article explores the essential building blocks of an enterprise-grade QA automation framework, focusing on maintainability, scalability, reliability, and integration with modern DevOps workflows.

Why Enterprise QA Requires a Different Approach

Unlike small applications, enterprise systems typically consist of multiple services, APIs, web applications, background workers, and third-party integrations. A successful automation strategy must support:
- Repeatable and deterministic test execution
- Parallel execution across multiple environments
- Continuous validation within CI/CD pipelines
- Cross-browser and cross-platform compatibility
- Reliable API and end-to-end testing
- Actionable reporting and failure diagnostics
Core Components of an Enterprise Automation Framework

A mature automation framework generally includes several layers working together:

Test Architecture

A modular test architecture separates test logic, reusable components, configuration, and reporting. Common patterns such as the Page Object Model (POM) or Screenplay Pattern help improve maintainability as test suites grow.

API Testing

API validation provides rapid feedback and verifies business logic before UI testing begins. Automated API tests are typically integrated into every deployment pipeline to detect regressions early.

UI Automation

User interface automation validates complete user workflows while remaining independent of implementation details whenever possible. Stable element identification and synchronization strategies help reduce flaky tests.

Test Data Management

Reliable automation depends on predictable test data. Isolated environments, seeded databases, and automated cleanup procedures help ensure repeatable execution.

Continuous Integration

Automation delivers the greatest value when executed automatically as part of every build. CI pipelines should provide immediate feedback while generating reports that allow teams to identify failures quickly.

Best Practices
- Successful enterprise QA automation initiatives commonly follow these principles:
- Prioritize API tests before UI tests whenever possible.
- Keep automated tests independent and deterministic.
- Avoid duplicated test logic through reusable components.
- Execute tests in parallel to reduce pipeline duration.
- Monitor flaky tests and continuously improve stability.
- Treat test code with the same engineering standards as production code.
Common Challenges

Organizations implementing large-scale automation frameworks often encounter:
- Long execution times
- Flaky end-to-end tests
- Poor test data management
- Environment inconsistencies
- Difficult framework maintenance
Addressing these challenges requires disciplined engineering practices, continuous refactoring, and close collaboration between development, QA, and DevOps teams.

Conclusion

Enterprise QA automation is not simply about increasing the number of automated tests. Effective frameworks provide fast feedback, improve software quality, reduce deployment risk, and support continuous delivery at scale. By combining sound architecture, reliable automation patterns, and CI/CD integration, engineering teams can build testing platforms that remain maintainable as applications continue to evolve.
October 6, 2026
AI-Native Testing Is Now a Core Quality Engineering Discipline

Predictions from some of the most prominent voices in the technology industry suggest that software engineering could become increasingly obsolete within a year, jolting the developer community in ways that are still unfolding. The ripple effect spreads further across social media and news outlets, with some leaders urging developers to start seriously considering alternative career paths before the window closes.

But while AI can generate functional code in seconds, it still cannot handle complex decision-making, understand ambiguous real-world requirements, navigate legacy systems, or take accountability for the behavior of large interconnected systems. Those limitations matter enormously in production environments where the stakes are high and the edge cases are unpredictable.

If AI is writing code faster and in far greater volume than any human team ever could, then the surface area of what needs to be tested does not shrink but actually explodes, fundamentally changing how teams approach validation, failure diagnosis, and quality coverage at scale. LeadDev cited research finding that AI coding assistants can be up to four times faster than humans but also ship code that is ten times riskier without strong review and testing processes in place.

Because the faster the development pipeline moves, the more critical and more burdened the quality engineering function becomes, and that is exactly why AI-native software testing did not just quietly evolve last year. It reshaped everything from how tests are authored to how failures are diagnosed and how entire teams are structured around autonomous agents.

Agents Are Replacing the Testing Framework Layer

Srinivasan Sekar, Director of Engineering at TestMu AI, formerly known as LambdaTest, explains that the industry is moving beyond simple code generation to a state where agents possess genuine agency and can act on the environment rather than just describe it.

The real breakthrough, Sekar argues, lies in the Model Context Protocol, an open standard that allows AI agents to interact directly with browsers and products in a standardized way across the testing environment. This protocol enables agentic QA, where an agent can execute a test on a website without needing a pre-written automation framework like Selenium or Playwright. And because these agents use visual perception and reasoning to understand the application rather than relying on rigid object locators, they are less likely to fail just because a UI element moves or a CSS class changes.

What that shift produces at the team level is a testing stack that is becoming less about the framework and more about the intelligence driving it. Srinivasan points out that generative AI is emerging not just as a content creator but as a logic engine that can synthesize complex user behaviors, allowing for a level of exploratory testing that was previously impossible to automate. An agent can now behave with the curiosity and intuition of a skilled manual tester, navigating an application in ways that no pre-written script could anticipate.

Teams moving in this direction are not just replacing tools but fundamentally changing what automation means in their workflow, shifting from brittle scripts that require constant maintenance toward resilient workflows that adapt to changes automatically.

From Co-Pilots to AI-Native Teams

Angshuman Rudra, Director of Product and Solutions at TapClicks, describes adoption as a movement through distinct levels of maturity that organizations must navigate deliberately rather than rush. Many teams start by using tools like GitHub Copilot for basic prompting and quickly hit a ceiling because base models lack context about specific business rules, which means the output they produce requires as much correction as it saves in effort.

Angshuman argues that the agentic phase is where the trajectory changes, because it introduces retrieval-augmented generation into the testing pipeline, allowing agents to retrieve specific test cases, Jira tickets, and defect history to understand custom business processes rather than applying generic testing logic. A standard model might know how to test a login page, but only a RAG-enabled agent understands the specific compliance requirements of a banking application built on years of accumulated business rules.

The final stage is the AI-Native phase, where agents communicate with other agents to drive the software development lifecycle while humans move into a strategic orchestrator role focused on architectural decisions rather than script syntax. Angshuman notes that this orchestration layer is where the real value gets unlocked, because it enables asynchronous workflows where agents can work overnight to prepare reports and surface bugs before the human team logs in.

That shift in how engineering time gets allocated, away from execution and toward direction, is what separates teams running AI-native workflows from teams that have simply added AI tools to an unchanged process. Getting there requires treating adoption as a maturity journey with deliberate investment at each stage rather than expecting the final state to emerge from a tool rollout.

Taking the Daily Grind Out of Test Maintenance

The practical impact of these agents is most visible in how we handle the daily grind of test execution and maintenance. Sai Krishna V who is also a Director of Engineering at TestMu AI highlights that AI-native testing significantly reduces the plague of flaky tests that has haunted the industry for years. He explains that because agents use reasoning to identify UI elements they do not fail just because a button changed its color or position.

Krishna explains that the focus is shifting toward intelligent root cause analysis, where agents classify errors and suggest fixes automatically, allowing engineers to review an AI-generated analysis that pinpoints exactly why a test failed and whether the cause was a real bug or an environment issue rather than spending hours parsing logs manually.

The self-healing capability that underpins this changes the economics of software maintenance in a way that compounds over time. Krishna observes that in traditional environments a significant portion of engineering time disappears into maintaining technical debt in the test suite, and that debt grows as the application evolves because every change creates new opportunities for brittle locators to break. With AI-native execution, that maintenance burden shifts to agents that continuously update their understanding of the application as it changes, freeing senior engineers to concentrate on feature development and architectural work where human judgment is genuinely irreplaceable.

Teams that have made this shift report not just reduced maintenance overhead but a change in how engineering capacity is perceived and allocated across the organization.

Testing the AI Itself

As businesses integrate generative AI into their own products, the testing surface area grows in ways that traditional quality frameworks were never designed to handle. Dr. Ravikiran Nizampatnam, Expert Network Security Engineer at Interpublic Group, warns that validating AI-embedded applications requires a completely new set of safety metrics that go well beyond confirming functional correctness. Testers must now evaluate for hallucinations to verify that the AI provides factual information rather than fabricated data, and for bias and toxicity to ensure that model responses remain safe and ethical across diverse user bases. Performance benchmarking for custom RAG solutions is equally essential because a slow or insecure model damages user trust just as quickly as a functional bug.

Dr. Ravikiran identifies prompt injection attacks as perhaps the most critical security concern in this new testing surface, where malicious users attempt to manipulate the AI into revealing sensitive internal data or performing unauthorized actions. Traditional security testing tools are ill-equipped to catch these semantic vulnerabilities because they were built to test deterministic systems where the same input always produces the same output.

Validating a chatbot response means checking not just whether it answered but whether the answer is contextually relevant, tonally appropriate, and free from manipulation artifacts that static testing would never surface.

Dr. Ravikiran argues that organizations need specialized agents designed to red-team their AI applications on a continuous basis rather than treating AI security as a one-time audit that gets checked off before launch.

Overcoming the Human Barrier

The transition to an AI-native approach is primarily a human challenge rather than a technical one. Many workers naturally fear job displacement or feel overwhelmed by the rapid pace of change in the industry. But the solution is to adopt a crawl, walk, run approach where you start with small pilots to prove value before expanding. There is a critical need for universal education in prompt engineering because the most successful teams will be those that learn to direct these powerful new agents effectively.

And leaders must proactively manage this cultural shift by framing AI as a force multiplier that removes the drudgery from their daily work. It is essential to show teams that by offloading the repetitive tasks of script maintenance they can focus on high-value activities like user experience research and architectural review.

The teams that thrive going forward will be those that embrace this hybrid model where human creativity is amplified by machine precision and scale.

We are standing at the precipice of a new era in software engineering where the definition of quality is being rewritten by artificial intelligence. The question for engineering leaders is no longer whether they should adopt these tools but how quickly they can integrate them. Those who hesitate risk being left behind by competitors who are already operating at the speed of agents.

July 23, 2026
Why DIY Test Automation Succeeds Its Way Into a Problem

Ask any engineering team if they can build their own test automation framework, and the answer is almost always “yes.” With modern AI tools involved, that answer arrives faster and with more confidence than ever before. In 30 days, a capable team can spin up scripts, automate flows, generate test cases, and show a demo that genuinely impresses decision-makers.

So, the case for building a homegrown test automation framework looks airtight… at first. But the demo is rarely where the story ends.

The true reckoning tends to arrive around day 90, when the framework that proved the concept starts demanding the attention of a product. Because the real concern isn’t whether the DIY testing automation framework works in week four; it’s if the organization is prepared for what it’s signed up for by week 40.

Why Early Success Is the Setup

What makes the first 30 days of a DIY test automation platform so compelling is exactly what makes the next 90 so expensive. Early wins such as rapidly generated test scripts, automated regression flows, and climbing visible coverage numbers, look like momentum. But they quietly accumulate into an engineering obligation for which nobody budgeted.

A test framework that produces results will also create reliance. The framework’s scope will expand to meet new requirements as its role shifts from being lightweight tooling into becoming something closer to infrastructure for stakeholders.

Somewhere in that progression, a “tool” becomes a “platform,” without ever receiving the budget, ownership, or governance of one. And therein lies the trap.

The danger here is not that a DIY framework fails. It’s the opposite: the danger is that the framework succeeds just enough to become business-critical before anyone has consciously decided to build and maintain a full validation platform.

The Costs That Don’t Initially Appear

The first business case for building usually compares licensing cost against engineering effort. Engineering effort tends to win on paper, but that comparison is incomplete.

The full lifecycle cost of a DIY validation framework includes a long list of line items that rarely make it into the initial spreadsheet: scheduling and orchestration logic, test data setup and teardown, parallel execution across environments, retry logic and failure analysis, access controls, and reporting pipelines.

Then the ongoing maintenance load arrives. Browser versions change, operating systems update, and SaaS vendors push releases on their own schedules with no obligation to preserve existing automation behavior. Authentication flows evolve. AI agents enter workflows and alter how systems behave in ways nobody anticipated when the framework was first designed. The cumulative cost of responding to all of it, across months and years, is where DIY frameworks quietly outspend the purpose-built platforms they were meant to replace.

There is also an opportunity cost that tends to be invisible at decision time. Every senior engineer pulled into maintaining internal test orchestration infrastructure is an engineer not building the product features, customer experiences, or competitive capabilities the organization actually exists to deliver. That reallocation of attention compounds over time in ways that are difficult to quantify in advance and painfully obvious in retrospect.

The Question That Changes the Decision

When the build-versus-buy decision arrives, the right question is not whether the team can build a test automation framework. Skilled engineers can build almost anything. The right question is whether the organization is prepared to own and evolve that framework indefinitely.

Once a framework becomes business-critical, it needs a roadmap and someone to be held accountable when it breaks at 11pm before a major release. The decision to build is also a decision to absorb all of that, permanently. That full scope is worth naming explicitly before the first line of code is written.

A useful principle for drawing the line: build where the capability creates genuine strategic differentiation for the business, and buy where the capability is necessary, expensive to maintain, and not core to what the organization does. Validation infrastructure almost always belongs in the latter category. Organizations that treat it as the former tend to discover that distinction at considerable cost.

The Practical Takeaway

None of this is an argument against using AI to accelerate quality engineering. AI genuinely improves the speed of test creation, and that improvement is worth capturing.

The teams that capture it most effectively tend to be the ones who think clearly about the difference between test creation and test operations. Creation is getting faster. Operations remain as complex as they’ve ever been, and they grow more intricate as software change velocity increases.

The organizations that navigate this well certainly ask, “can we build this?” But they also ask a harder question at the outset: “what do we want our engineering capacity focused on for the next three years?”

Teams that answer the latter question honestly, before the first demo clears a sprint review, tend to build quality capability that actually scales. The ones that skip it often spend those same years maintaining a framework that was never supposed to be a product.

Speed and quality at scale come from knowing where to build and where to buy. The engineering teams that get this balance right are deliberate about what they choose to own, and they’re honest about the full weight of that choice before they make it.

June 3, 2026
Applying TestOps to Cultivate a Quality Culture

Quality assurance is a universal concern, especially when it comes to application development. Enterprise applications are faster and more reliable than ever before. However, in addition to performance, application developers also must consider data security, regulatory compliance and cost-effectiveness, among other things. Organizations are embracing quality engineering (QE) as a foundation to create a software quality culture to address these challenges. Creating a quality engineering or TestOps environment is not simple, especially when QE departments must test for hundreds of applications across multiple enterprise environments. Whether it is a solution provider or a multinational corporation, applications often need to be tested across platforms, industries and geographies.

TestOps encompasses a broader concept than just QE. With TestOps, there are testers and engineers using automated and manual testing to validate software for flawless application performance. In enterprise DevOps, the TestOps team serves as the bridge between development and deployment since they have a good understanding of the applications and a better perspective on the context and variables that can affect performance.

Continuous testing is an integral part of any software development cycle. Quality engineers must be brought in early as part of Agile development to keep development on track and head off any problems that could escalate or become harder to resolve later. In Agile, testing becomes more crucial and frequent to ensure flawless performance.

It pays to think horizontally when setting up a TestOps approach. For example, testing should be performed across different vertical environments such as media, manufacturing and finance. The goal is to create a test environment that can accommodate various industries.

TestOps Across Environments

In the age of SaaS and universal data access, it is essential to test across multiple platforms. In addition to ensuring enterprise performance, mobile web and remote data access must be tested. For streaming services, it is vital to test across mobile platforms, over-the-top (OTT) platforms such as Roku, Apple TV, Android TV and Fire TV, and even gaming consoles like PlayStation and Xbox.

When shipping software updates, geography matters (especially when shipping across time zones), as one may never know when the software will be used or have control over the environment where they plan to use it.

For instance, consider the challenges a leading mortgage lender faced with mobile application testing and how geography was a factor. First, Google Analytics was used to assess specific device and user requirements and ascertain performance in various parts of the United States since different regions use different carriers. Testing was then done across mobile platforms such as iOS, Android and combinations and across carriers since most banking and mortgage applications required one-time passwords (OTPs) and SMS-related capabilities. Further tests were implemented for new identity authentication strategies such as biometrics and facial recognition. Finally, performance testing by geography was conducted to determine whether a new feature or version would create issues.

Testing for Geographies and Regional Compliance

Localization testing is important since every region and every country has unique compliance issues and laws. To ensure proper localization, it is essential to perform complete testing, including lower environments and production systems.

In any TestOps environment, the philosophy of continuous testing should already be part of the design environment, including understanding the nuances of production. Release readiness does not mean there are only a few known bugs in certain regions. It means the application has been tested for compliance in every geography where it could possibly be utilized.

Testing Across Industries

Testing application performance in specific industry settings brings along its own set of unique challenges. In manufacturing, for example, each sensor in an IoT device must be modeled against a conformance specification to determine if it can be automated using simulations. In media, one needs to test for different platforms, content management systems and business workflows.

To accelerate time to market, one would want to support a dual-shift development lifecycle to gauge the consumer experience and get continuous feedback and also need a test environment with multiple automation tools such as Selenium, Appium, Cypress, Tricentis, UiPath, Test Complete and Winium.

Artificial intelligence (AI) plays a larger role in testing, shortening time to market, and ensuring superior quality software. AI-driven test tools are optimized for domain-specific applications, such as testing for industries like media, manufacturing and fintech. AI can fill in the gaps to achieve optimization and offer a quicker turnaround with limited manual intervention.

Any organization cultivating a TestOps culture needs to have specific technologies and tools in place. When assessing your testing technology stack, it is important to create an end-to-end testing ecosystem. This means the environments must be inclusive and capable of incorporating testing for unique applications, environments and scenarios. To support the needs of today’s Agile application development environment, one must plan for an agile testing infrastructure to keep development on track.

July 27, 2022
Creating Career Opportunities in Quality Engineering

Software testing is the thread that connects code to customers, ensuring that the code created by development teams works for users and supports customer loyalty. But as the demands of software development are transformed by DevOps and customer expectations rise, the career and role of the software tester is evolving rapidly. Customer expectations have made it imperative for applications to work seamlessly, necessitating evolution in QA and testing.

As the QA and software testing sector progress—and “quality engineering” becomes the de facto term for creating agile, customer-centric software teams—QE is attracting a more diverse pool of leaders as the career skillset widens. The more quality engineering becomes the differentiator between elite software teams that thrive in a DevOps world and teams that struggle to adapt, the more important it becomes to elevate diverse leaders in the industry.

Understanding this, mabl recently held a panel discussion with QA and software testing industry veterans who discussed the new era of quality and how these changes are transforming who enters the field.

The Evolving Role of the Software Tester: Building Professional Confidence

The constant evolution in software testing and quality engineering, though exciting, also presents a challenge to QA professionals: How to consistently build the necessary skills as well as the confidence to succeed. As quality engineering becomes a multidisciplinary career role that encompasses engineering, functional and non-functional quality management and the customer experience, the question for QE professionals becomes one of continuous learning and the confidence to broach, if not lead, challenging discussions.

LaDeitra Lee, chief technology officer for Sparx Studio, understands how confidence can sometimes feel elusive—especially for younger pros just getting started. But her message is meant to encourage those just starting out to speak up: “QA knows the most about the product because we know the customer’s perspective, and we’re testing the full product from the front end that everybody sees all the way to the backend … the main thing: You want to make sure you have the confidence in your work,” Lee said.

When quality engineers have confidence in their skills, they’re better able to collaborate with the rest of the software development team to improve testing strategy and triage defects. But building confidence can be challenging, especially as software testing has embraced coding skills to the point where software developers can specialize in quality as SDETs. However, that doesn’t mean that coding experience is the most essential skill in a quality engineering career. Since quality engineering is all about connecting code to user context, soft skills like empathy are just as critical for the next generation. So, even if QE professionals aren’t expert coders, they can build confidence in their skills by understanding their unique place in the software development process.

Marjie Carmen, software engineering manager at Arch Insurance, agreed. “When I started out, it was commonly believed that software testers didn’t need programming skills or software engineering skills since the focus was on manual testing. But today, I see QA working hand-in-hand with and having almost the same skills as a software engineer; sometimes even more. I really see quality assurance as equal because they have to know the business side and the perspective of the users and where the industry is going. They have to make really good recommendations about how the product should be and need the confidence to go to the engineering side and say, ‘What’s the fastest way I can get this product tested and in the hands of the user?’”

Creating Unconventional Career Paths in Software Development

How has hiring for a tester today changed in the last decade? As quality engineering continues to grow as a profession, many in the field are creating new career paths by moving between different roles throughout the software industry. By supporting these unconventional candidates, Maaret Pyhäjärvi, principal test engineer and prominent QA speaker, said that software development organizations can grow internal talent and help quality engineering develop as a field.

“We hope that there’s more of this openness for moving from one technical role to another,” said Pyhäjärvi. “There’s always the possibility of learning small slices of technicality. Some of my favorite architects these days are so good at software testing; so good at programming and can really bring the whole team together. So we need to allow that kind of movement where people get to try things and break away from stereotypes.”

Susan Marie, president of the Triangle Software Quality Association (TSQA) said, “I love the capital-Q quality emphasis because it gives me leverage to do well—that’s just one area of quality. I hate the idea that the job title is the one thing that you’re allowed to do; you need to be giving guidance around what’s expected of you and what other people can expect from you.”

While technology is always evolving, former Slack senior engineering manager Karine Sully said the main characteristics for the job have been consistent—even as far back as a decade ago. “I always look for people who are curious and who have good communication and organizational skills, especially coming in at entry-level or lower-level. All you need is to be able to think critically and organize your thoughts and be able to communicate what it is you’re doing. Then, you can push beyond that and grow.”

Given the challenges inherent to working with multi-disciplinary teams, Lee said, “I think what helped me the most is having a sponsor. So my advice for anyone on my team, or anyone that’s looking to grow in their career, is making sure you have someone a few levels above you looking out for you.”

Fostering Diversity in Software Testing Teams

Growing responsibilities equal a growing number of requirements and skills for quality engineering careers. Navigating the hiring process is never easy, but succeeding in a rapidly changing field can feel like running a marathon at a full sprint, especially when trying to foster more diversity in software development and testing.

“…Diversity and inclusion are definitely very dear to my heart, and I feel you have to go where people don’t look like you. So if you want to hire more Black or Hispanic engineers, you have to go to areas where you see more of them,” Lee said. “If it’s engineering that you’re looking for, do your research and find those schools that specialize in that particular area. I’m a part of the National Society of Black Engineers, so I tap into that network if I’m trying to help build a diverse team.”

Carmen agreed that hiring managers need to look closely at areas where there may be holes to fill. “I got into management by looking at gaps and filling them—it was always my goal to find a gap and say, ‘Hey, I can take that off your hands and in the role.’ So it’s a natural evolution to get the different roles that I’ve had by saying yes to more and different responsibilities.”

Conclusion

Though the QA and software testing fields are in the midst of several transformations, the change is opening the door for more people to have successful careers in software quality. Though their role is to ultimately improve the product, that road to quality is usually filled with bugs and unpredictable challenges. When quality engineering professionals are confident in their work, it’s easier for them to lead constructive conversations on improving the product—and facing down the engineering teams, if necessary. Building a diverse and confident next generation for quality is essential to building delightful digital experiences for all.

“I heard from a very famous pioneer in the engineering industry about QA that ‘We don’t have a magic wand; we can’t test quality into your product, but we can test your product.’” Pyhäjärvi said. “The whole concept of QA engineer, QA quality assurance, QE, I think gives people the illusion that we can do something impossible. We can only show light to the areas of the product that need work.”

June 27, 2022
Quality Engineering Has DevOps and Agile in the Driving Seat
As opposed to the traditional software development lifecycle (aka waterfall), today’s Agile and DevOps-based software development process is more outcome-oriented. The unrelenting pace of the changing market landscape has made the hybrid, Agile plus DevOps way of software development absolutely critical in addressing customer demands and staying competitive.

The focus on Agile and DevOps is necessary due to the high failure rates of traditional software development methodologies. These failure rates are mainly attributed to the glitch-infested, low-quality applications churned out by following legacy methodologies.

The failure rates had left many companies to bite the dust and brought the key role played by quality in ensuring customer satisfaction and ROI for the business into sharp focus. Also, the IT landscape is witnessing developments such as mergers and acquisitions, increased focus on risk and compliance, data and analytics, the fast rollout of apps and mobile commerce, among others. These have necessitated the software development process to become increasingly agile, streamlined, collaborative and flexible. The reshaping of customer expectations is letting organizations to go beyond shift-left testing or DevOps testing by transforming the QA process into one driven by quality engineering.

What Is Quality Engineering?

To meet the growing demand for quality software applications in double quick time, a mere tweaking of the software development process is not enough. What is needed is the adoption of software quality engineering process comprising optimal quality assurance, predictive analytics and monitoring of QA elements to reduce glitches to negligible levels. The process would involve a continuous cycle of feedback and quality improvement based on the same. The quality engineering process offers maximum test coverage in the shift-left scheme of things and ensures outcomes like functionality, security, accessibility, usability, performance and reliability of the software application. It complements Agile and DevOps methodologies and ensures their outcomes to remain consistent with the desired business objectives. Quality engineering, instead of identifying the inherent glitches in the SDLC, focuses more on preventing them altogether.

Shift Left and Strengthen Right

Quality engineering services cover the entire spectrum of the SDLC thereby yielding the maximum test coverage and delivering the best quality product. With DevOps testing shifting left, the quality determinants are incorporated right into the product build during the development phase. At the same time, DevOps-based QA focuses on the shift-right approach to ensure the last stage of the SDLC i.e., delivery goes on unhindered and customer feedback is taken in right way and worked upon. The ultimate aim of both shift-left and right is to create and deliver a glitch-free product that meets the requirements of the end-users. Here, since end-users are the ones who ultimately assess quality of a software product based on parameters such as usability, functionality and performance, among others, the feedback mechanism should be responsive enough to understand the issues faced by the end-users and remedy them at the earliest.

Quality Engineering as an Aid

Quality engineering (QE) helps to bridge the gap between development and deployment processes, which can arise due to the lack of agility. It harps on the successful strategy of “test early, test often,” to pre-empt challenges that businesses face when testing a software product. Quality engineering ensures the quality of a software application throughout the SDLC–right from the designing stage to its final delivery. QE can be incorporated into the development process by using methods such as Behavior-Driven Development (BDD), Test-Driven Development (TDD) and Acceptance Test-Driven Development (ATDD) respectively.

Let’s find out what these methods are all about:
- BDD: In this software development method, the behavior of both the software and business outcomes are taken into consideration. Here, the right sets of tests are automated, leading to an increased collaboration among developers, testers and other business stakeholders.
- TDD: In this software development process, the QA team writes unit level tests before the development stage. This results in getting an early feedback and helps the development team to refactor its requisites.
- ATDD: This software development process focuses on writing an acceptance test even before the development phase. This helps the software to meet the acceptance criteria beforehand.
Quality Engineering and DevOps

QE, being an end-to-end testing methodology, can offer optimum solutions to businesses struggling with their legacy IT infrastructure. The solutions in terms of continuous testing and integration as mandated by DevOps, can be arrived at by integrating the QA process with the DevOps pipeline. This results in executing processes such as build, test, deploy and deliver in a seamless manner. QE complements DevOps and Agile by detecting glitches right at the beginning of the development process.

Moreover, QE’s focus on end-to-end testing helps the product to be developed and tested simultaneously. This approach adds teeth to the DevOps’s requirement for continuous integration (CI) and continuous delivery (CD). No wonder, the successful implementation of quality engineering is underpinned on fulfilling the requirements of DevOps and Agile. It further consolidates the benefits achieved from implementing DevOps testing services and Agile testing services.

QE riding on DevOps and Agile can help businesses to scale up their operations without investing much resources and time. By implementing DevOps test automation and Agile test automation using a test automation software such as Selenium, Robitium etc., businesses can boost their SDLC. QE can help businesses to develop and deploy glitch-free software speedily and deliver enhanced customer experience.

Conclusion

The fast-changing competitive business landscape of today requires organizations to streamline their operations, increase productivity and fast forward the delivery of glitch-free software applications. To achieve these objectives, they need to make quality engineering as part of their development and testing cycles. However, QE ends up complementing the Agile and DevOps approach by achieving outcomes such as CI and CD. Furthermore, it leads businesses to get high ROI, better productivity, reduced cost of operations and enhanced customer experience.

— Akshaya Choudhary
October 15, 2019
Incorporating Performance Engineering into DevOps
Performance testing historically has been a challenge for most IT organizations. It is highly misunderstood, from executives down to the few resources allocated to perform an extremely difficult task. Most companies consider performance testing expensive and unnecessary and simply refuse to acknowledge it is a critical component of the software development life cycle. Have we forgotten about major performance failures such as Healthcare.gov? It is time companies start to take performance engineering seriously.

IT organizations using the Waterfall methodology relied on a few performance testers to develop performance scripts and run them after most of the testing had been completed and the application was a few weeks from a production deadline. If the performance team was lucky, they would get in one or two successful tests, typically working off-hours and executing performance tests on small, patched-together ancient hardware. The problem with this approach is when issues are found, there is not enough time to fix them adequately and performance degrades over time.

Developing a Performance Engineering Strategy

Here are some items to consider when incorporating performance engineering into DevOps:
- Learn—It is important to learn what performance engineering is and how it can be incorporated into your organization. If you don’t have the right skills within your organization, go find someone who can help. There are many organizations that can help with an initial assessment of where things stand.
- Develop Your Strategy—This is perhaps were most teams fail. Upfront planning is critical and it will save you in the end. It is important to develop a performance engineering road map. This is not something that will be built in a few weeks; a solid performance road map could take 18-24 months, so be patient and build it right.
- Build Your Baseline—It is important that you build a baseline of your performance tests so that you can determine if you have successful results. Performance testing is different from functional or automation because you have to consider the performance run as a whole. A few high transactions usually are acceptable. It is a good idea to build a history of runs to see how the application performance is trending.
- Identify Key Business Workflows—It is too time-consuming to build hundreds of performance tests, so focus on those that are the most critical to your business. Typically those workflows will account for the majority of your performance load.
- Test Upfront—With DevOps you need to start early and you will need to run performance tests frequently. The more you can run upfront, the more performance issues will be avoided in production.
- Tools and Hardware—Performance engineering requires tools and hardware. It is important that your assessment evaluates what you currently have and what you will need to do it properly. There are many open-source tools on the market today that can help and other tools that are making a big difference in the performance space.
- Budget—Developing a world-class performance engineering practice is going to take money. When you develop your performance road map, you might need two to three budget cycles to get the tools and infrastructure you need.
- Load Performance Environment—Yes, you will need to have a performance environment that is scaled to match your production environment. If there isn’t enough budget, then you will have to know how much smaller the environment is so that you can be confident in your performance results.
- Data—It is important to have data that is similar size to your production environment. This will help you understand how your system will handle performance within the production environment.
Incorporating performance engineering within DevOps will require work, but the rewards will certainly put the organizations that embrace it at the top of the list. DevOps enables teams between Dev and Ops to produce a stable application that performs exceptionally well under load. Here are some benefits of DevOps performance engineering:
- Predictable production performance results
- Production deployment confidence
- Eliminate catastrophic performance failures
- Increased performance efficiency
DevOps gives organizations a real opportunity to get performance engineering right; unfortunately, most organizations will carry over the same mistakes into DevOps. I hope you will take the opportunity to embrace the performance engineering challenge!

About the Author / Ron Wilson

Ron Wilson is a passionate champion of Agile, DevOps and Quality Engineering. He has managed large global Quality Engineering teams across multiple industries. He is currently the director of Quality Engineering at Arch MI. He regularly shares insights through social media where you can find him at www.qarevolution.com and on LinkedIn.
September 1, 2017
In DevOps, Quality is a Team Effort

Technology is changing every industry; we can all agree on this foundational premise. If you disagree, I believe the rest of this post is going to do nothing for you, so feel free to stop reading now. If you do agree, then you know that apps, the internet of things (IoT) and APIs are table stakes now. Businesses need strategies for each, and the forward-thinking executives are already adapting. The ones that aren’t risk alienating a channel in their overall digital strategy. It’s not all about strategies for building new types of functionality and partnerships; organizations also need to validate they will work specifically with their customers’ use cases in mind.

When we study software development, the history books teach us that waterfall strategies followed a linear path. In the past, this pace may have worked, but the future seems bleak with the evolution of technology and its disruption of traditional, manual business processes advancing at an exponential rate. Over time, the stages in the software development life cycle (SDLC) may remain the same, but organizations need to focus specifically on becoming more efficient at completing more work in less time. With this “do more with less” mindset, it’s no wonder many organizations see a backlog of work piling up. This point is especially true for quality assurance and quality engineering teams.

First, let’s unpack the software development stages from a traditional sense. Historically, organizations would get an idea and that idea could come from the businesses stakeholders (sales, marketing, customers, etc). The business would prioritize/stack rank all of their ideas by which carried the most merit and how much work those ideas would require for implementation. This is known as the “planning” phase. From planning, the idea would progress to be built. “Building” is the part of the life cycle where your artists/engineers live. You know, the creative people, the developers. They build the technology according to specifications that come from the planning phase. Most importantly, they are incentivized based on the project being delivered on time.

After the technology is built and packaged, it is passed on to the team that has been traditionally responsible for quality—quality assurance (QA). QA has their time with the product to validate the product works as intended, can scale to the appropriate demand levels, is secure and meets all the use cases without error before they pass it over to the team that will deploy it to the hands of their customers. Finally, the business is measuring feedback and customer sentiment, and then rolling that feedback back to the plan stage for improvement.

As time has passed, we have also seen another major trend develop. Organizations have developed agile/iterative SDLC strategies that are focused around bringing smaller chunks of functionality to their users/customers faster. Just like “lean manufacturing,” this concept has been focused predominantly around helping the developers get their projects completed faster. The primary challenge with waterfall development strategies is that by the time an organization brings the plan to market, the needs and priorities of the customer will have already changed. The speed of idea generation outpaces the execution of said ideas. That being written, while development has kept pace, quality has not, unfortunately.

Now I pose a simple, albeit, extremely important question: Who owns responsibility for ensuring quality of the finished product? If your answer is QA (like stated above), and I can see why that would be the instinctual response—the process is set up for failure. Here’s the fundamental reason why: if quality is so important to the process, then why is it the third phase in the SDLC? This implies planning and building without quality in mind is an acceptable process.

Quality should be owned by everybody in the process. QA/QE teams are your enablers of embedding quality throughout the SDLC. The distinction is more about instituting an ideology or culture based on quality.

With a culture focused on driving quality, when the planning team works on creating a specification for the idea, they should work with development and QA/QE as they are designing the flows, behaviors of the application. This allows for QA/QE teams to understand what to validate much earlier in the process. Teams are always looking for ways to factor more automation into the process. QA teams are also looking for ways to make their test environments more closely resemble the production environment where their customers interact with the business.

Unfortunately for QA/QE, they feel pressure from the business to execute within a constrained time frame with better results. This typically means inadequate windows of opportunity to start building automated tests or an incomplete environment that doesn’t quite resemble what the end user will face.

The last frontier to validate is performance which answers the important question: Will the app scale for the expected user population? This is often the last step before flipping the switch and shipping the product to your customers. But what happens if the application doesn’t scale? Organizations need to drive performance validation earlier. Better yet, they should engineer the application for the desired performance, and that means—yes, you guessed it—taking care of this earlier in the life cycle.

What if it were possible to drive more improvement?:
1. … To start building functional test plans and test automation at the time when planning happens?
2. … To ensure that development and test environments more accurately have access to the constrained downstream resources that cause unexpected “wait time”?
3. … To ensure that the automated testing that will be executed in the testing phase, has the right data to power the tests?
4. Finally, … to make sure that performance isn’t just an afterthought but a strategy to scale to the changing landscape of a digital business?

It’s more than possible. It’s obtainable. This process is known as continuous testing. It’s about the validation that needs to occur earlier—not just within a window of allotted time within an overall life cycle. It’s also important to understand that it’s a maturity curve, and it will take work to evolve your individual process.

Can your organization embrace continuous testing strategies? If not, what’s stopping you? If so, where can we begin to improve? Driving quality isn’t just about your company’s KPIs—it’s about your customers’ perception of your brand and what you do for them. You never get a second chance to make a first impression. Let’s build the right impression today!

— BobbyMelwani

August 15, 2017
You Asked for Quality, You Got Alienated QA

The role of Quality Assurance (QA) is creating a bit of a buzz these days, because even as teams and budgets are shrinking, the demand for better quality is increasing. The chilling repercussions of service disruptions caused by software flaws warrants interest even at the executive level: “Nobody’s madder than me about the fact that the website isn’t working as well as it should,” said President Obama when HealthCare.gov went down last year due to software defects.

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November 24, 2014