Non-functional testing is testing software for its non-functional requirements: the way a system operates, rather than specific behaviors of that system. [1] This is in contrast to functional testing, which tests against functional requirements that describe the functions of a system and its components.
Accessibility testing is a non-functional testing activity that verifies whether a system, website, or application can be perceived, operated, and understood by people with a wide range of disabilities and whether it meets objective accessibility criteria such as the Web Content Accessibility Guidelines, WCAG success criteria. It typically combines automated checks, to detect obvious technical failures, manual inspection, to evaluate semantic structure, keyboard navigation, and ARIA usage, and human usability testing with people who have disabilities to assess real-world effectiveness and usability. Many government and organizational web standards now require WCAG conformance and explicitly treat accessibility as a mandatory non-functional quality attribute of public-facing digital services. Practical guides and industry overviews describe accessibility testing as a specialized subset of usability and non-functional testing that focuses on legal conformance, inclusive design, and measurable success criteria rather than functional feature behaviour alone.* [2] [3]
Baseline testing is a non-functional activity that establishes a measured reference for key quality attributes (for example, response time, throughput, resource usage, error rates, and availability) against which future changes, releases, or configurations are compared. It usually occurs early in a release cycle or after a major environment change and combines controlled synthetic tests (benchmarks, scripted workloads, profiling) with monitored production observations to capture representative operating conditions. The baseline results are recorded as pass/fail thresholds, performance budgets, or trend series so regressions, capacity drift, and configuration-induced degradation can be detected quickly and triaged. Best practice is to store baseline test artifacts (test scripts, input datasets, environment definitions, and raw metrics) alongside the release so reruns are reproducible and comparisons remain meaningful across time. [4] [5]
Soak testing involves testing software under significant production or production-like load for an extended period of time.