QA for AI Systems vs Traditional Software: Key Differences and Practices

Focusing on the End-to-End ML Lifecycle in QA ## AI-Specific Bug Categories and Failure Modes ## Best Practices for Testing AI Functionality and Performance ## Conclusion After talking to QA experts, we arrived at the same conclusion: testing AI-driven applications is much more complicated than testing traditional software. In classic deterministic systems, QA relies on predictable behavior and clear pass or fail rules. AI models do not really follow that logic, since the same input can lead to different outputs. For example, a generative AI feature may return slightly different results every time it runs, and those results can still be acceptable. Because of this, QA cannot depend on one fixed expected output and instead has to work with acceptable ranges and quality thresholds rather than exact matches. QA has to stay involved for the long run and keep watching how the model behaves as data and conditions change. In real projects, AI QA is not something you finish at release and move on from. Models can slowly lose quality as input data shifts or the environment changes. Because of that, QA does not really end once the model goes live. Quite often, that is when the most interesting problems begin to surface. Testers need to think ahead and prepare for continuous monitoring in production, along with regular regression checks when models are updated. Many teams underestimate how quickly this work grows. AI QA also changes what is expected from testers themselves. In their daily work, QA engineers work closely with data scientists, reviewing datasets together, checking annotation quality, and trying to interpret model metrics as a team. Over time, testing AI systems turns into a much broader responsibility. It is not just about validating code, but about understanding data flows, model behavior, and how the system actually performs for users as it evolves. AI QA does not stop at the UI or API. In real projects, testing ends up covering the whole ML lifecycle, and that changes how QA work actually feels day to day. AI systems tend to fail in ways that feel unfamiliar if you come from classic software testing. Because of that, it helps to group AI bugs by where they actually come from. This makes test planning more practical and reduces blind spots. Unlike traditional QA, where testing largely ends before release, AI QA continues throughout the system’s lifetime. In most projects, AI issues usually fall into a few broad buckets: data, annotations, the model itself, pipelines, and behavior on edge cases. Each of these shows up differently in testing. Testing AI is not the same as testing regular features. Some classic QA techniques still help, but many need adjustment, and new habits appear very quickly. Key practices that guide effective AI testing include: Most of this work is iterative. Many issues only become clear through shared investigation with data science teams, not through one-time test runs. Testing AI systems goes far beyond what most QA teams are familiar with. AI behaves differently: results are not fixed, data quality plays a huge role, and behavior often changes after release. Because of this, QA cannot rely on feature checks alone. Data validation, bias checks, and regular observation of real-world behavior become part of everyday testing. AI testing also changes how QA work is done. Instead of acting as a final gate, testing turns into an ongoing process. Testers need to understand how ML pipelines actually work, design realistic and varied scenarios, and stay closely aligned with data science teams as models and datasets evolve. This kind of work can be demanding, but it is necessary. Many AI problems are subtle and would never show up in classic test cases. They often come from data issues or unexpected behavior in edge situations. By focusing on the full ML lifecycle and adjusting testing practices, QA teams help ensure AI systems are not just usable, but dependable, fair, and safe. This is why QA for AI is increasingly treated as its own discipline and why it plays a key role in building trust in AI-driven products. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Data and annotations: Most issues start with data. QA usually begins by looking at datasets and spotting obvious labeling problems, bias, or preprocessing mistakes. Even small data issues can push a model in the wrong direction. In practice, teams catch many of these through quick spot checks or small sample reviews. - Model evaluation: Models are trained by data scientists, but QA helps keep evaluation realistic. That means checking whether validation data looks like real input, agreeing on what “good enough” means, and comparing new models with older ones. Regressions are common, especially around edge cases. - Pipelines and integration: AI features rarely live on their own. Data ingestion, inference, post-processing, and product integration all have to line up. When something breaks, it is often due to a wrong assumption or a format mismatch. End-to-end tests and basic logging usually point to the problem faster than anything else. - Production monitoring: Deployment is not the finish line. Once a model is live, QA watches how it behaves in production, tracks a few simple metrics, and notices changes caused by new data or usage patterns. This is often where the real problems appear. Over time, QA shifts from one-time checks to continuous evaluation. Data changes, models change, and testing has to adapt. - Data-related issues: A lot of AI problems trace back to data. Skewed datasets, missing examples, corrupted files, or accidental leakage can all quietly shape how a model learns. QA often notices this by looking at basic dataset stats, sampling records, or checking class balance. When a model keeps failing on a specific type of input, it is often a sign that the data for that case was weak or incomplete. Data issues are tricky because they rarely surface unless someone goes looking for them. - Annotation problems: When people label data, inconsistencies are almost inevitable. Conflicting or incorrect labels confuse the model and usually hurt accuracy. QA helps by reviewing labeled samples, checking for contradictions, and validating labeling rules. When evaluation results look odd, manually inspecting misclassified examples often reveals labeling mistakes rather than model flaws. - Model-level issues: Model bugs do not crash the system. They usually show up as unstable or poor predictions. Sometimes the model is too simple, sometimes training settings are off, or sometimes results vary too much between runs. QA contributes by validating evaluation results, comparing versions, and checking reproducibility. If performance drops after a change, it often points to a model-level problem rather than an integration issue. - Pipeline and integration problems: AI models rarely run in isolation. Bugs often appear at the boundaries between components, such as incorrect preprocessing, wrong input formats, or misuse of model output. End-to-end testing is essential here. Logging intermediate steps helps QA figure out whether the model produced a good result that was later misused, or whether it never received the right input at all. - Behavior and edge cases: These are the failures users usually notice first. Strange answers, biased outputs, or unstable behavior often appear only for certain inputs. QA explores this through edge-case testing, unusual inputs, and basic safety checks. The goal is not to remove all odd behavior, but to make sure failures are predictable and safe. Thinking this way pushes QA past simple UI checks. Rather than asking “does it work,” the real question becomes why it fails and where the problem actually comes from, whether that’s data, labels, the model itself, the pipeline, or how the model behaves. - Start with understanding what the AI is doing. Is it generating text, classifying images, or ranking results? That context shapes everything else. QA often works closely with data scientists early on to understand how the model behaves and what “good enough” actually means for the product. - AI output also needs to be judged from several angles. Accuracy alone rarely tells the whole story. Relevance, consistency, tone, and safety often matter just as much. For that reason, a lot of AI testing still depends on human judgment, especially for user-facing features. - Functional testing does not go away, but it changes. QA checks that the system responds, handles different inputs, and fails in a reasonable way. Since AI output can vary, tests are often repeated with different phrasing or inputs. Negative cases matter too, such as empty requests or temporary failures of external services. - Edge cases are where AI usually falls apart. Unusual or ambiguous inputs tend to expose unstable behavior. QA explores these scenarios to understand the limits and to make sure failures are predictable and safe. - Bias also needs explicit attention. Models can behave differently across user groups or data segments, and those differences are easy to miss without targeted checks. When bias shows up, it is treated as a real defect. - Performance is another concern. AI features can be resource-heavy, so QA looks at response times, memory usage, and behavior under load. Long-running tests often reveal slow degradation that short tests miss. - AI testing is closely tied to usability. Outputs need to be clear, actionable, and easy for users to correct or ignore. QA treats AI results as part of the product experience, not just backend responses.