Turn requirements, tickets, spreadsheets, designs, videos, and natural-language workflows into reviewed, traceable ContextQA tests.
Business problem: Product intent lives in tickets, documents, spreadsheets, designs, and demo recordings. QA must translate it into test coverage quickly without losing traceability or skipping negative scenarios.
ContextQA can use several forms of source material to create a test baseline. The workflow is most effective when the source remains linked to the generated cases, the AI can ask clarifying questions, and a human approves the result before it becomes a release gate.
Plain-English description
A known user journey or a newly discovered regression
URLs, setup state, user role, and explicit expected results
PRD, BRD, FRD, PDF, DOCX, TXT, or spreadsheet
Formal requirements and legacy manual test libraries
Requirement boundaries, field rules, omitted error states, and duplicated cases
Jira or another connected work item
Sprint stories, acceptance criteria, and bug reproduction
Ticket scope, linked work, priority, and traceability
Image, wireframe, or Figma design
UI behavior that is designed before implementation
Intended navigation, state changes, accessibility, and responsive behavior
Video walkthrough
A complex workflow best demonstrated by a subject-matter expert
Hidden prerequisites, pauses, data dependencies, and implied assertions
OpenAPI or API definition
Contract and integration coverage
Authentication, status codes, schema, boundary values, and chaining
Write the business outcome before uploading the source. For example: “A returning customer can renew a subscription using a saved payment method, and a declined payment leaves the original subscription unchanged.” This gives the generated tests a clear end state and includes a meaningful failure case.
Upload the primary requirement and any useful visual context. Select the target platform—web, mobile, or API—and choose the test types you need: positive, negative, edge, field validation, boundary, functional, or end-to-end.
For large legacy libraries, import a representative spreadsheet first. Verify the column mapping, parameterization, tags, and expected results before processing the rest of the library.
Answer AI clarification questions with concrete rules. Confirm user roles, environment, starting state, acceptable values, integrations, cleanup behavior, and what should happen when a dependency fails. If the answer is not yet known, record the gap with the product owner rather than allowing the test to invent policy.
Use Required Approval when a QA owner must review generated coverage. Check that each test has:
one clear business purpose;
the correct prerequisite and starting URL;
explicit assertions, not only navigation steps;
safe parameters instead of embedded credentials or personal data;
tags or links that preserve feature, ticket, and release traceability;
negative and edge cases that reflect the real cost of failure.
Place approved cases in a feature suite and a test plan. Run a focused smoke set first, then the broader regression plan. Use the first execution to identify missing data, unstable environments, or requirements that cannot yet be verified.
When requirements or code change, review linked tests and impacted areas. AI Insights can crawl an authorized application on a schedule to maintain application context and reveal new pages or flows that may need coverage.
See AI Insights for the current setup screen, scheduling fields, and journey-report workflow.
Inputs: a Jira story, checkout wireframes, payment API specification, and a short product walkthrough.
Generated coverage to expect:
successful renewal with a saved payment method;
declined card and expired card behavior;
displayed price, taxes, term, and confirmation details;
API response and persisted subscription state;
duplicate-submit protection;
email confirmation, when the test environment exposes a safe way to verify it.
Business value: Product, QA, and engineering share the same release contract. Reviewers can see which acceptance criteria are automated, which gaps remain, and which evidence supports the release decision.
Lead time from requirement approval to reviewed automated cases
Acceptance criteria with at least one linked test
Generated cases accepted, edited, or rejected during review
Escaped defects caused by a missing requirement or missing negative case
Reuse of imported test data, prerequisites, and steps across later features
Last updated
Was this helpful?
Was this helpful?