What Is QA Testing? Differences with QE and Evolution

Maximize your software quality with a deep dive into QA testing and QE. Discover strategies, AI integration, and real-world insights to boost results and transform your next product cycle.

Welcome to your Essential Guide to Effective QA Testing and its evolution into QE!

Quality Assurance Testing (QA) involves systematic activities designed to verify if software products meet defined requirements and standards. Historically, it was reactive, mainly focusing on identifying defects at the end of the software development cycle. However, as software development practices evolved, so did QA.

Quality Engineering (QE) is the advanced, proactive approach we adopt at Abstracta. QE integrates quality throughout the entire development process, from planning to deployment, focusing on prevention and continuous improvement.

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What is QA Testing?

Also known as software testing, QA traditionally involves detecting and reporting defects after software development. It validates software against specified requirements using structured methods, manual execution of test cases, and scripted automated tests. QA emphasizes meeting standards through established testing activities.

What is QE Testing?

QE is an integrated, proactive approach that embeds quality practices throughout the software development life cycle. It leverages continuous testing, advanced automation, and AI-driven methodologies to anticipate defects and streamline collaboration across teams, significantly enhancing the software quality process.

Differences Between QA and QE

Role of Software Testers in QA vs. QE

Traditionally, a software tester focuses on executing predefined test scripts and reporting bugs. In QE, testers become quality advocates involved early in the software development cycle, influencing decisions to prevent issues. QE testers employ automated testing methods, critical thinking, and deep architectural understanding.

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Manual vs. Automated Testing Methods

QA often relies on manual methods for actual testing, especially when validating new features or complex workflows. Modern QE places greater emphasis on sophisticated automated testing methods, combining automation with human creativity and detail when required.

Automation efficiently handles repetitive tasks and regression testing, integrating seamlessly into CI/CD pipelines. This enhances robustness and speed within a dynamic testing environment, while manual testing remains vital where human insight is needed.

Planning a Testing Strategy

A well-planned testing strategy is indispensable for effective software testing. At Abstracta, we prioritize test cases based on risk and impact, focusing on critical functionalities first. This approach empowers us to identify defects early in the development cycle.

We have developed a quality software testing maturity model, a comprehensive framework that helps teams improve the quality of their software testing efforts. It encompasses all aspects of software quality, including maintainability, functionality, automation, performance, accessibility, usability, and security testing, to meet the needs of users and stakeholders.

Need support for your test strategy? Check out our Test Strategy Services

Need support for your test strategy? Check out our Test Strategy Services

What’s The Difference Between Functional vs. Non-Functional Testing?

While functional testing focuses on validating specific features and software functionalities against defined requirements, non-functional testing assesses broader qualities like performance, usability, and reliability. Both testing types are necessary and complementary for creating quality software, in both QE and QA testing.

In the following sections, we’ll dive deeper into each type, exploring their unique roles, testing types, and how they work together to enable a robust and effective QA strategy.

What is Functional Testing?

Functional testing verifies that software features operate according to the requirement documentation, aligning with user expectations and delivering intended outcomes. It covers unit testing, integration testing, system testing, regression testing, exploratory testing, and user acceptance testing (UAT).

• Unit Testing: Tests individual code components.
• Integration Testing: Verifies that components or systems work together.
• System Testing: Evaluates complete, integrated systems.
• Regression Testing: Checks if new changes don’t introduce defects.
• Exploratory Testing: Actively explores software to uncover defects.
• User Acceptance Testing (UAT): Confirms software readiness for deployment based on user needs.

At Abstracta, we believe testing is a creative process. Our team takes advantage of their experience and critical thinking to explore how the software works, how it is used, and how it may fail.
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What is Non-Functional Testing?

Non-functional testing evaluates software beyond specific functionalities, focusing on performance, scalability, usability, accessibility, security, and compatibility.

• Performance Testing: Evaluates system performance under varying conditions.
• Load Testing: Assesses behavior under specific loads.
• Stress Testing: Determines robustness under extreme conditions.
• Usability Testing: Assesses user-friendliness and ease of use.
• Accessibility Testing: Fosters usability by individuals with disabilities.
• Security Testing: Identifies vulnerabilities and verifies software security.
• Compatibility Testing: Confirms consistent performance across various devices, browsers, and systems.

If you are interested in keeping on learning about this topic, we recommend you read this article: Differences Between Functional and Non-Functional Testing

Advanced QA and QE Testing Practices

In this section, we will explore advanced testing practices that enhance the efficiency and effectiveness of your testing efforts. From setting up the right test environment to leveraging AI in testing, these practices are designed to help you deliver high-quality software consistently.

Observability Testing

Observability testing allows QA and QE teams to monitor software behavior using logs, metrics, and traces, enhancing performance, security, and usability. This practice provides immediate feedback on software status, aiding quick decision-making and issue resolution.

Observability also extends to data, focusing on monitoring the health and quality of data within your systems, which is critical for maintaining data accuracy and reliability.

At Abstracta, we joined forces with Datadog to leverage real-time infrastructure monitoring services and security analysis solutions. Check our solutions here!

AI in QA Testing

Artificial Intelligence agents play an increasingly crucial role in both QA and QE practices. AI-driven testing methods, such as automated test generation, defect prediction, and visual testing, optimize and enhance the entire testing process, offering deeper insights and predictive capabilities to reduce risks and accelerate testing cycles.

Here are some tasks that may be carried out by AI agents:

• Test Case Generation: Automatically generate test cases based on user stories and requirements. This helps in covering more scenarios and reducing the time spent on manual test case creation.
• Defect Prediction: Quickly analyze historical data to predict where defects are likely to occur. This helps in prioritizing testing efforts and focusing on high-risk areas.
• Test Automation: Enhance test automation by identifying which test cases to automate and optimizing test execution. This helps in reducing the time and effort required for test automation.
• Visual Testing: Perform visual testing by comparing screenshots of the application to identify visual defects. This helps in verifying the UI is consistent and free of visual issues.

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Measuring QA and QE Effectiveness

Teams often look at metrics and dashboards, but the real insights emerge when people gather to interpret what those numbers are telling them about how they work and what users actually experience.

• Defect detection rate opens the door to honest discussions. When the team reviews this number, it’s not about pride or blame. Instead, they start asking which kinds of bugs slip through and why, who was part of the conversation when requirements were defined, and how early feedback is reaching the people who need it most. Shifts in this rate can reveal changing dynamics between roles, communication habits, or even cultural changes within a team.
• Test coverage percentage becomes meaningful only when paired with an understanding of business risk and context. Coverage reviews trigger dialogue about what parts of the application matter most right now, whether recent changes have shifted priorities, and which areas have quietly become technical debt.
• Mean time to failure is never simply a metric. When the number drops or spikes, it prompts the team to retrace their steps: how were recent deployments managed, what patterns are emerging in incidents, and how do those findings shape the way future testing is planned and resourced?
• User satisfaction scores lead teams outside the technical sphere and back to real users. These conversations can uncover subtle usability issues, hidden friction in workflows, or places where expectations diverge from the product’s promise. In the best teams, this feedback becomes part of sprint planning and even day-to-day standups.

When teams embrace this kind of metric-driven storytelling, quality moves from the realm of compliance to a daily, shared pursuit.

Effective Test Environment Setup and Testing Setups

Behind every reliable release is a set of test environments and setups designed not just for coverage, but for real confidence and learning. These setups reflect what teams value: speed, realism, collaboration, and adaptability.

• Realistic and dynamic environments are shaped by ongoing conversations with operations, developers, and testers. People invest in mirroring production data, infrastructure, and continuous integration points—not for the sake of complexity, but to reveal the hidden, unpredictable failures that only emerge in true-to-life scenarios.
• Repeatable and consistent test setups make it possible to spot true regressions and improvements. Teams work together to document, automate, and reset environments between runs. This way, every test becomes a reliable source of insight rather than a guessing game.
• Flexibility in provisioning—enabled by containerization, cloud solutions, or dedicated tooling—it lets teams pivot quickly. When teams find a critical bug, environments can be spun up instantly for focused investigation or collaborative debugging.
• AI-powered environment management starts surfacing in advanced teams, where recent incidents, usage patterns, or business changes drive automated setup adjustments, enabling each cycle to target what’s most relevant.

Testing environments are more than infrastructure. They are where teams take risks safely, learn quickly, and build the trust needed for confident releases.

Collaboration and Quality

The heartbeat of any quality initiative is the way people work together across roles, backgrounds, and perspectives. When testers, developers, product owners, and operations sit at the same table, the focus moves beyond finding defects toward creating shared understanding and genuine progress.

It’s in these ongoing exchanges (standups, impromptu pairing, honest retrospectives) where silent assumptions are surfaced and the unexpected is made visible. Teams that actively open their process invite richer questions: What did we learn from this release? Who saw the risk before it became a problem? Where did we miss the mark for our users, and why?

Over time, these conversations create a rhythm of trust and responsiveness. As the feedback loops tighten, everyone, from business to tech, gains a clearer sense of how their daily choices contribute to robust, meaningful outcomes. This is how quality becomes a living, breathing part of the culture, not a checkbox or afterthought.

Transition from QA to QE

No organization becomes a quality engineering powerhouse overnight. The move from QA process to QE approach begins quietly, often as a handful of people start embedding questions about quality earlier in the workflow and exploring the limits of their tools and processes. Curiosity grows into intent as teams start inviting testers into design discussions, letting automation handle the tedious, and making space for more exploratory, creative work.

Over time, patterns emerge: automation becomes smarter, testing becomes less about final verification and more about enabling speed and confidence at every step, and AI starts to illuminate trends that were once invisible. People learn to value early warnings, not just late-stage fixes, and see every failure as an opportunity for adaptation. As these practices take root, the culture shifts.

Quality is no longer delegated to a single role or phase. It’s infused throughout the lifecycle, supported by continuous learning and honest reflection. The transition is, at some point, a direction to keep moving: more integrated, more resilient, and ultimately more impactful for both users and the business.

FAQs about QA and QE Testing

What Is Meant by QA Testing?

QA testing is the practice of using structured testing methodologies and clear testing procedures to assess software quality. Teams incorporate QA testing into every test planning phase to maintain quality control, monitor quality metrics, and ensure future product testing cycles deliver reliable results.

What Does a QA Tester Do?

A QA tester designs and executes test cases, performs detailed testing, and documents defects. Often, QA testers working simultaneously with development and operations teams collaborate on component testing, supplement manual testing with automation, and support further testing until release.

What Is the Basic QA Testing?

Basic QA testing includes the setup and execution of component testing, creation of test cases, and review of outcomes against requirements. It covers every step from the initial test planning phase to the execution of testing setups and quality control reviews.

How Do Teams Incorporate QA Testing in Agile Environments?

Agile teams incorporate QA testing by embedding QA testers in each sprint. This allows for continuous feedback, iterative improvement, and real-time execution of test cases using both manual and automated approaches.

Why Is Component Testing Important in QA?

Component testing isolates and validates individual units of software, making it easier to identify and resolve issues early. This form of detailed testing is crucial for robust releases.

What Is QE Testing?

QE, or Quality Engineering, is a holistic approach that integrates quality practices across the entire software development life cycle. QE encourages development and operations teams to work together, use data-driven quality metrics, adopt advanced testing methodologies, and automate detailed testing procedures. The focus is on preventing defects early, supporting future product testing cycles, and achieving results-oriented QA testing at every stage.

How Do Development and Operations Teams Collaborate in QE?

In QE, development and operations teams partner with QA testers from the test planning phase onward. Together, they design testing setups, execute test cases, and share insights from real incidents. This collaboration builds dynamic testing environments, strengthens quality control, and ensures feedback from both sides shapes every release.

How We Can Help You

With over 16 years of experience and a global presence, Abstracta is a leading technology solutions company with offices in the United States, Chile, Colombia, and Uruguay. We specialize in software development, AI-driven innovations & copilots, and end-to-end software testing services.

Our expertise spans across industries. We believe that actively bonding ties propels us further. That’s why we’ve forged robust partnerships with industry leaders like Microsoft, Datadog, Tricentis, Perforce BlazeMeter, and Saucelabs, empowering us to incorporate cutting-edge technologies.

Our holistic approach enables us to support you across the entire software development lifecycle. Visit our solutions page and contact us to discuss how we can help you grow your business.

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