What SQA Stands For
SQA stands for software quality assurance. The formal IEEE definition is "a set of activities designed to evaluate the process by which products are developed or manufactured." In plain terms, it is the work of making sure software is built in a way that produces good results consistently, not just once.
That is different from testing, though the two get confused constantly. Testing finds defects in a product. SQA looks at the process that produces the product. A team can have thorough test coverage and still have a broken development process generating defects faster than anyone can catch them. SQA addresses that upstream problem, not just the output.
In practice, most SQA professionals do both. They write and run tests, but they also own quality planning, process evaluation, standards compliance, and risk reporting. The title "SQA engineer" or "SQA analyst" usually signals someone embedded throughout the development lifecycle rather than someone who shows up at the end to check a finished build.
An SQA expert is someone who can own that work at a strategic level. Not just executing a test plan someone else wrote, but building the quality strategy, identifying where the process is breaking down, and communicating risk clearly to people who never open a bug tracker.
What the Day-to-Day Work Looks Like
The specifics vary by team and product, but experienced SQA professionals tend to cover the same ground across organizations.
Test planning and strategy. Deciding what gets tested, how thoroughly, at which point in development, and by which method. A test plan that looks complete on paper but cannot be executed with the team's real capacity is not a good plan, regardless of how thorough it looks in the document.
Test design and execution. Writing test cases, running them, and evaluating results. This is the core craft. Well-designed tests find real problems. Poorly designed tests produce a passing board while genuine issues ship into production.
Defect tracking and root cause analysis. Logging defects clearly enough that engineers can reproduce and fix them, and more importantly, identifying patterns. When the same type of failure keeps appearing in the same area of the codebase, that is a process signal, not a series of unrelated bugs.
Process review. Evaluating how the team works and where quality risk is concentrated. This is what separates quality assurance from quality control. Quality control is inspection after the fact. Quality assurance works before and during development, with the goal of preventing defects rather than only finding them.
Risk reporting. Translating technical findings into language that product managers and engineering leads can act on. This is the part of the job that matters most for career progression and the part most people underinvest in.
The Job Market Context
Before getting into how AI has reshaped the role, it helps to know where the profession stands. According to the U.S. Bureau of Labor Statistics, software quality assurance analysts and testers had a median annual wage of $102,610 in May 2024. Employment across the broader category, which includes software developers, QA analysts, and testers, is projected to grow 15 percent from 2024 to 2034, which the BLS classifies as much faster than average.
That is not a profession in decline. It is a profession in transition. The growth projections were published during the same period AI coding tools went mainstream, which tells you something about what the BLS expects AI to do to QA demand: create more of it, not less.
How AI Reshaped SQA Expertise
For most of software testing's history, the underlying assumption was simple: given the same input, a system produces the same output, every time. That made it possible to define expected results, write deterministic tests, and grade outcomes as pass or fail.
That assumption does not hold when a product contains AI components. A customer support chatbot, a document summarizer, a recommendation engine: none of these behave deterministically. The same input can produce different outputs on different runs. Both outputs might be fine. One might be subtly wrong. Standard pass/fail testing cannot sort that out reliably.
Two separate problems follow from this, and keeping them distinct is useful.
The first is a volume problem. AI coding tools are generating code faster than most teams can review it carefully. Sonar's 2026 State of Code survey, which polled over 1,100 professional developers globally, found that 42 percent of committed code is now AI-assisted or AI-generated, and that 96 percent of developers do not fully trust it is functionally correct. Only 48 percent say they always verify it before committing. That is a significant amount of material entering production without confident human review.
The second is a methodology problem. When the product itself uses AI, the approach to testing has to change fundamentally. You cannot check AI output against a fixed expected value. You need evaluation methods that assess quality within an acceptable range, catch failure modes specific to AI systems, and keep watching behavior after release. What is AI testing covers the methodology shift in full, including why traditional QA methods fall short and what replaces them.
SQA expertise in 2026 means holding both skill sets. The traditional fundamentals still matter and still form the foundation. They are just not the whole job anymore.
The Failure Modes SQA Professionals Need to Know
If your products include AI features, there is a set of failure modes you need to understand that simply did not exist in traditional software. These are not theoretical edge cases. They show up in production and cause real problems for real users.
Hallucination. An AI model produces information that is factually wrong, invented, or untraceable to any source. The output reads confidently, looks plausible, and is incorrect. Hallucination testing is a dedicated discipline focused on checking whether model outputs are grounded in trusted source material and flagging content the model appears to have fabricated.
Prompt injection. A user crafts input that manipulates the AI system into doing something outside its intended behavior: bypassing guardrails, exposing data it should not, or triggering unintended actions. What is prompt injection explains how these attacks work and what testers look for when evaluating AI-enabled products for this class of vulnerability.
Bias and inconsistency. A model gives different quality responses based on demographic signals in the input. If a hiring tool, a loan product, or a medical application behaves differently depending on who is asking, that is a bias failure. It will not appear in a standard functional test. Finding it requires deliberate evaluation across a varied and representative range of user inputs.
Non-determinism. The same input produces different outputs across runs. Not every variation is a defect. Acceptable variance is part of how these systems work. But distinguishing acceptable variation from a genuine quality failure requires a different testing approach than traditional QA was built for.
Model drift. A model's behavior degrades over time even when the underlying code has not changed. Usage patterns shift, the model gets updated by its provider, and performance that was fine at launch quietly declines. This is why AI quality cannot be verified only before release. It requires ongoing production monitoring, which does not fit neatly into a traditional pre-release test cycle.
OWASP's AI Testing Guide, version 1.0 released in November 2025, addresses all of these as testable risk categories with structured methodologies, not theoretical concerns. NIST's AI TEVV program takes a complementary approach, advancing measurement science for AI and conducting evaluations across accuracy, robustness, bias, and interpretability. Both are worth understanding if you are doing serious AI quality work.
What Separates Experience from Expertise
Time on the job matters, but it does not automatically produce expertise. Plenty of QA professionals with years of experience have not updated their methodology for AI systems. And there are newer practitioners who have built serious skills quickly because they worked on the right products at the right time.
What distinguishes SQA expertise, regardless of tenure, is judgment under uncertainty. That means knowing when a defect is a one-off versus a signal of a process problem upstream. It means designing test coverage that reflects real product risk rather than whatever is easy to measure. It means understanding the failure modes specific to AI systems, not just the ones traditional software produces. And it means communicating quality risk in terms that technical and non-technical stakeholders can both understand and act on.
There is also a more recent dimension: evaluating AI-generated test artifacts critically. Teams can now produce test cases with AI assistance, which saves time. It also introduces a new failure pattern: tests that look comprehensive but miss real product risk because the model generating them did not understand the domain well enough to know what mattered. An SQA professional who can identify those gaps is doing something qualitatively different from one who accepts the output because it was fast. How to test LLM applications gets into this in practical detail.
Where Credentials Fit In
SQA expertise is hard to verify from the outside. A resume that says someone led QA for five years does not tell a hiring manager whether they have worked on AI-enabled products, whether they understand non-deterministic testing, or whether their methodology has kept pace with how software is built now.
Structured credentials help address that gap. ISTQB is the global benchmark for software testing knowledge, and most employers who care about standardized testing skill look for it. According to the BLS Occupational Outlook Handbook, the field is growing and the market is competitive enough that credentials matter when distinguishing between candidates with similar experience on paper.
ASTQB AI Assurance Pro™ is a designation built on three ISTQB certifications: Foundation Level, AI Testing, and Testing with Generative AI. For SQA professionals who already have hands-on experience, it is a way to demonstrate that your methodology covers AI-specific testing, not just traditional QA carried into a domain it was not designed for. AI Assurance Pro for testers covers the career case in more detail, including where the designation is most useful and where experience alone tends to be sufficient.
Credentials do not replace project work or the ability to communicate clearly about risk. They work best as a structured, verifiable layer on top of real experience, making your background readable to someone who cannot assess depth from a resume alone.
Where to Go from Here
If you are already in QA and trying to figure out whether you qualify as an SQA expert, or whether you are on the path toward it, the bar is moving. Traditional QA knowledge still matters and still forms the foundation. AI-specific methodology is now part of what strong candidates bring to teams shipping AI-enabled products.
For most people already in the field, the practical path looks like this: build depth in test design, defect analysis, and risk-based thinking. Those skills do not expire. Get grounded in what makes AI systems different to test by starting with how AI is changing software testing. Get specific on the failure modes: hallucination, prompt injection, bias, non-determinism, and drift. And if you need a structured credential to signal that your skills have moved with the field, the ISTQB path remains the clearest option available.
If you already hold ISTQB Foundation Level, you are closer to the AI Assurance Pro designation than you might expect. The remaining pieces are two AI specialty exams: ISTQB AI Testing and ISTQB Testing with Generative AI. How to get the ASTQB AI Assurance Pro™ designation covers the exam sequence, scheduling, and the designation request process.
Next step: If you are weighing the ISTQB certification path, the three required certifications page breaks down each exam and what it covers.
Keep reading: How to Become an AI Tester · AI Testing Certifications Compared · How to Evaluate AI Testing Skills When Hiring