QA vs QC vs QE: The Complete Breakdown of Software Quality Assurance

In a digital era where users expect zero glitches, poor-quality software is a business killer. According to the Consortium for IT Software Quality (CISQ), software quality costs the U.S. economy at least $2.41 trillion. The message is clear: SOFTWARE QUALITY IS NOT OPTIONAL. To ensure quality, you need to understand QA vs QC vs QE. These three terms are often used interchangeably, but they are different in approaches.

In this blog post, we’ll help you understand these key differences differences between QA, QC, and QE (quality assurance vs quality control vs quality engineering) when it comes to quality assurance vs quality control vs quality engineering. Ready to build secure, scalable, and user-centric software solutions? Let’s dive in!

What is Quality Assurance (QA) in Software Testing?

QA is the discipline of establishing rules and processes to ensure that a software product is built, deployed, or launched correctly. It is a proactive approach, which means it prevents potential defects or failures by improving software testing and development. That is, QA is a method-oriented approach to ensure that you are doing things right. This helps you develop, launch, and deploy user-friendly and industry-compliant software solutions.

Software quality assurance is crucial because:

• It’s time- and cost-effective as early defects are less expensive and usually quicker to fix.
• It supports compliance as standardization of processes is done in regulated environments.
• It supports culture whereby each person is responsible for quality, and not a testing team.

The AI twist in QA: With today’s Agile and DevOps testing ecosystems, AI in QA has evolved beyond manual checklists. For instance, AI-powered QA tools like Testim and Applitools can now predict defect-prone areas, generate intelligent test cases, and perform continuous monitoring.

What is Quality Control (QC) in Software Testing?

QC is applied to ensure that a software product satisfies the quality requirements specified. It is a post facto process with emphasis on locating defects in completed products by thorough testing of software and inspection.

That is, QC assists you in verifying whether the development process you use is satisfactory to you and whether there exist any bugs or deviations in the software specifications.

Software quality control is crucial because:

• It validates readiness of software releases through successful identification of functional, usability, and performance bottlenecks.
• It provides feedback to improve QA and development workflows (closing the loop).
• It develops stakeholder confidence, helping them understand what the software really does and whether it is good enough.

The AI twist in QC: AI-backed QC tools automate test execution and identify visual/UI bugs using machine learning. Automation frameworks like Selenium, Cypress, or Playwright ensure that manual testers spend less time repeating tasks and more time analyzing results.

What is Quality Engineering (QE) in Software Testing?

QE is a unified, engineering-based approach to quality that employs several quality practices across the entire software product development life cycle (SDLC). QE is the way of the future in QA and QC, merging proactive as well as reactive techniques to achieve greater quality.

To put it simply, quality engineering in software testing infuses quality into the product from the very beginning using technical tools, automation, and collaboration to prevent defects rather than detecting them later.

Software quality engineering is crucial because:

• It focuses on shift-left, diagnosing problems early while scaling quality via automation.
• It encourages continuous feedback and cross-collaboration between Dev, QA, and Ops.
• It is quite aligned with existing DevSecOps/CI/CD practice and AI testing services for ongoing assurance.

The AI twist in QE: AI-driven tools can analyze commit histories, predict failure-prone modules, and automate performance and security checks in real time. Frameworks like Jenkins, GitHub Actions, and Katalon TestOps bring in QE to every stage of the SDLC.

QA vs QC vs QE: Key Differences You Need to Know

Quality Assurance vs Quality Control vs Quality Engineering: How Do They Work Together?

In a successful software lifecycle, QA establishes the standards and processes, QC tests to qualify the product, and QE provides quality embedded in design, code, and operations. Companies can start with baby steps, starting with QC, flowering into QA, and ultimately having an end-to-end QE model. Read how each adds differently towards ensuring software quality:

1. Data Protection

QA establishes encryption levels, access control policies, and rules for compliance to secure data at all times. These rules or standards may include ISO 25010, ISTQB, or TMMi.

QC then ensures these standards are enforced, inspecting code for secure coding practice while ensuring that encryption algorithms conform to requirements.

QE, in return, is included in security testing as part of the process of construction in reality.

Practical Example: QA mandates that all client-server data being communicated should be encrypted using TLS 1.2 or above. QC verifies the usage of the code properly with TLS and peer reviews to identify possible security bugs. Meanwhile, QE automates penetration testing and the continuous testing pipeline to simulate real-world cyberattacks, detecting weak points before they reach production.

2. User Interface (UI) and User Experience (UX)

QA sets usability standards like accessibility, consistency, and navigation, building an integrated and usable user experience.

QC verifies those standards through usability testing, obtaining real-user input to check if the interface aligns with actual users.

QE augments UI and UX while integrating them into continuous delivery pipelines. This helps detect UI regressions at an early stage and make the experience smooth across all browsers and devices.

Practical Example: QA enforces stringent font readability design specifications, color gradients, and button positioning. QC conducts live user test sessions, observing the way customers interact with the interface. QE implements automated visual regression testing to identify format changes following each software release, ensuring consistency in design.

3. Compliance and Regulation

QA develops a checklist to test the product’s adherence to regulations like HIPAA or GDPR.

QC conducts audits to ensure the product is complying.

QE enhances compliance by embedding automated compliance checks and data-handling validations into the development workflow. This reduces manual effort and human error.

Practical Example: QA enforces a compliance checklist like data encryption, consent handling, and retention policy according to GDPR. QC manually inspects the product from time to time for compliance and flags non-compliance. QE performs system checks for compliance, automatically verifying data encryption and consent logs on every code.

4. Integration and System Interoperability

QA sets the integration standards, from API authentication to error handling. It enables seamless communication among systems.

QC tests integrations intensively to ensure systems exchange data and handle exceptions gracefully.

QE builds test automation around such integration tests so that any deployment or update will not break existing connections. It relies on advanced AI-powered frameworks like Mabl, Functionize, and TestSigma to redefine continuous quality.

Practical Example: QA app testing sets up API authentication and normalized data exchange policies. QC confirms these connections by adding end-to-end testable third-party payment gateways. QE tests the APIs routinely post-deployment using Postman and automated CI/CD testing software, ensuring error-free communication and smooth system execution.

5. Continuous Improvement and Feedback Loop

QA extracts learnings from each growth cycle and translates them into tangible process improvement recommendations.

QC preserves learnings as test results and audit conclusions, and teams can thus see where there were discrepancies with expectations and why.

QE responds to these learnings by using them to automate low-value work and leverage AI-powered analytics and feedback, embedding a culture of ongoing improvement.

Practical Example: QA has repeated UI faults on record for process enhancement. QC identifies the root cause of the defect through defect analysis. QE performs automated UI regression testing to identify the problems sooner in the upcoming sprints so that similar flaws will not be seen in subsequent releases.

Conclusion

QA builds the foundation for quality. QC ensures that the foundation holds up under scrutiny. QE scales and sustains quality through test automation and innovation. Together, they don’t just make your software “bug-free” but make it reliable, secure, scalable, and ready to shine in the real world. So, are you ready to redefine software quality?

Partner with QASmartz—where QA, QC, and QE come together to deliver future-proof digital experiences. We combine human expertise with AI intelligence to redefine quality assurance. Talk to our QE consultants for a free AI-readiness assessment today!

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