What Is Quality Assurance in Software Engineering?

Quality assurance (QA) in software engineering is the set of processes that prevent defects from reaching a release, not just catch them afterwards. Quality control and testing are narrower disciplines within it. QA works best when it is built into every sprint, on a small custom software development project and on an enterprise platform alike. This guide covers what QA involves, how it differs from QC and testing, and how AI is changing it.

Quality assurance, quality control and testing: what's the difference?

Quality assurance is the process-level discipline that stops defects from being created in the first place. Quality control is the product-level check that catches defects once they already exist, and testing is the practical activity QC uses to find them.

The three terms are often used interchangeably in job titles and vendor pitches, but they work at different levels of the same effort.

The distinction also matters for staffing. A team that hires only testers and calls it "QA" usually gets strong quality control and weak process discipline, which is the gap that lets defects like the ones below reach production.

Why does QA matter for a software team?

Skipping QA does not remove defects. It moves the cost of finding them from a sprint review to a production incident.

On 19 July 2024, a logic error in CrowdStrike’s own Content Validator let a mismatched sensor update pass its checks and reach 8.5 million Windows machines. The resulting IT outage was the largest on record. Parametrix estimated that US Fortune 500 healthcare companies alone suffered $1.94 billion in direct losses. The update had passed multiple levels of testing, yet it shipped with the kind of defect testing is meant to catch.

Knight Capital learned a related lesson in 2012. According to the SEC, it lost more than $460 million in 45 minutes after dormant, untested code was reactivated on a single server during a deployment. The same root cause, inconsistent deployment across environments, still appears in incident reports today.

For teams that handle regulated or sensitive data, QA and cybersecurity consulting overlap: a missed validation step is both a quality problem and a security exposure.

What are the main types of software testing?

Each type of software testing targets a different stage of the development lifecycle, from reviewing requirements before any code exists to validating the finished product with real users.

Requirements reviewing

This step happens before development starts. QA specialists and stakeholders walk through the specification together, checking that requirements are complete, consistent and testable. Catching an ambiguous requirement here costs an afternoon. Catching it in production costs a rebuild.

Unit testing

Developers test the smallest testable pieces of code, usually a single function or method, in isolation. It's the cheapest form of testing to run and the first line of defence against regressions.

Integration testing

Once individual units work on their own, integration testing checks that they work together. This is where mismatched data formats, broken API contracts and timing issues tend to surface, long before a user ever sees them.

System testing

System testing evaluates the whole application as one connected product, checking that it satisfies the original requirements end-to-end rather than component by component.

Exploratory testing

In exploratory testing, testers work without a script and use the software the way a real user might, looking for edge cases that written test cases did not anticipate. Because it is unstructured, it catches problems that automation misses.

User acceptance testing

User acceptance testing (UAT) happens last, in an environment that mirrors production as closely as possible. The goal isn't finding bugs so much as confirming the software solves the business problem it was built for, with real workflows and real data volumes.

Our guide to the seven principles of software testing covers the principles that shape how QA teams prioritise this work.

When should QA start in a software project?

QA should start the moment requirements are being written, not once code is ready to ship. This approach is often called shift-left testing, and it exists because the cost of a defect rises the later it's found.

A defect caught during requirements review might take an hour to fix. Caught by a customer after release, the same defect can mean a rebuild and a support queue.

In practice, QA runs through four phases:

Requirements and specification review

QA joins before a sprint plan exists, checking the documentation itself for gaps and contradictions.

Continuous testing during development

Unit and integration tests run alongside coding, not as a separate phase bolted on at the end.

Structured QA gates before release

System and acceptance testing confirm the product is ready, with clear criteria for what "ready" means.

Post-release monitoring

Defects introduced by later changes get caught through regression testing and production monitoring, not customer complaints.

This staged approach is often called the software testing life cycle, and it runs in parallel with development rather than after it.

SDLC vs STLC: what's the difference?

The software development life cycle (SDLC) covers how a product gets built, from planning through to deployment. The software testing life cycle (STLC) runs alongside it and covers how that same product gets verified.

A common planning mistake is to treat the STLC as part of the SDLC’s final phase instead of a parallel track. That is usually how testing gets squeezed into the last two weeks before launch. For a breakdown of each development phase, see our guide to the software development life cycle.

How is AI changing software QA in 2026?

AI is starting to move QA from a purely human, script-driven discipline towards one where autonomous agents write, run and maintain much of the test suite, and people set the quality bar instead of running every check by hand.

EPAM’s Agentic QA, launched in 2025, pairs autonomous testing agents with human oversight and reports throughput of up to ten times that of manual testing on comparable projects. According to the World Quality Report 2025-26, 43% of organisations are experimenting with generative AI in QA, but only 15% have scaled it across the enterprise.

QA specialists still decide what counts as good enough for a given product. Agents are only as useful as the quality objectives they are given.

In Go Wombat’s AI services and solutions work, agents speed up the repetitive parts of quality engineering and machine learning models flag anomalies that would take a person hours to spot, but a specialist still decides what ships.

How Go Wombat approaches QA in software engineering

Exit-grade engineering discipline

Go Wombat is ISO 27001-certified, and QA is part of the certified process. Every release comes with documented test evidence that will hold up under acquisition due diligence.

QA embedded from the Discovery Phase onward

Requirements review starts in the Discovery Phase, long before a sprint plan exists, so ambiguous specifications get caught while they still cost an afternoon to fix.

Testing built for data-heavy, physical-world systems

Go Wombat's engineers test software that talks to industrial IoT sensors, high-load AdTech platforms, and manufacturing floor systems, where a missed edge case has consequences beyond a broken button.

QA doesn't stop at launch

IT support and maintenance keep monitoring and regression testing running after go-live, because a defect introduced by a later change is still a defect.

Talk to our engineers about your testing gaps

Our software testing services team can scope a one-off audit or an embedded QA process for a data-heavy platform with you.

Key takeaways

QA is a process discipline, not a final check before release. The earlier it starts, the cheaper defects are to fix. AI agents are taking on repetitive testing work, while specialists define what quality means for each product.