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A reusable prompt for software testing
Start with a template that makes the task and its evidence explicit. Replace the bracketed text with project-specific information before sending it:
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Act as a [testing role] reviewing [feature or system]. Context: [product behavior, user roles, dependencies, environment, and relevant constraints]. Source requirements and acceptance criteria: [paste them here]. Task: [specific testing task]. Include [positive, negative, boundary, and relevant failure scenarios]. Do not assume behavior that is not stated; list open questions separately. Return [table, Gherkin, or framework code] with [required fields]. For every case, show the linked requirement, setup, action or input, expected result, and assumptions. Mark uncertain cases for human review.
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This structure follows the practical principle that a prompt should be clear, specific, and provide enough context for the model to understand the request. See OpenAI’s prompt-engineering guidance and the ISTQB sample exam for examples of structured testing prompts.
Prompts for common testing tasks
Generate test cases from a requirement
Using the requirement and acceptance criteria below, draft test cases for [feature]. Include normal use, invalid input, boundary conditions, and relevant state or permission variations. For each case provide an ID, linked criterion, setup, steps, test data, expected result, and assumptions. Separate directly supported behavior from questions that need clarification.
Requirement and acceptance criteria: [paste text]
A useful output can be reviewed row by row for requirement traceability. If a case has no supporting requirement or product rule, ask whether it is a valid expectation or an open question rather than treating the model’s suggestion as authoritative. A PractiTest prompt guide likewise suggests requesting test names, descriptions, steps, expected results, typical cases, and edge cases.
Find negative, boundary, and unexpected-input cases
For this requirement, identify negative, boundary, and unexpected-input scenarios. For each, state the precondition, input, expected safe behavior, and the requirement or product rule that supports that expectation. If expected behavior is unspecified, flag it instead of inventing a rule.
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Negative tests are most useful when their expected outcomes are grounded in a rule. For example, an invalid value might be rejected, normalized, or accepted under a documented condition; the prompt should not decide which behavior the product ought to have without evidence.
Draft Gherkin from a user story
Act as a test analyst specializing in Gherkin. Use the user story, acceptance criterion, and examples below to draft scenarios in Given-When-Then format. Keep each scenario aligned with the stated criterion, include expected outcomes, and label assumptions or uncovered behavior.
User story: [paste story]
Acceptance criterion: [paste criterion]
Examples or constraints: [paste if available]
ISTQB’s 2025 sample exam illustrates a prompt built around a user story and acceptance criterion, with role, inputs, constraints, and requested output. Review each scenario for alignment rather than assuming that valid-looking Gherkin is a valid test.
Draft unit or automation tests
Draft [language and framework] tests for [function or behavior]. Use the code and requirements below. Cover the stated success and failure behavior, boundary inputs, and relevant dependencies. Include setup, execution, and assertions. Do not invent APIs or fixtures; identify missing information. Explain which requirement each test covers.
Code and requirements: [paste relevant material]
Treat generated automation as a proposed draft. Run it in the intended project, check fixtures and dependencies, and confirm assertions test the required behavior rather than merely reproducing the implementation. The prompt guide from PractiTest includes automation-script prompts, but it does not validate code in your project.
Choose regression tests after a change
Given the change summary, affected components, dependencies, known risks, and existing test inventory, identify tests to rerun and explain the relationship between each selection and the change. Group by impact or risk, flag missing coverage, and list assumptions separately.
Change summary: [paste]
Components and dependencies: [list]
Existing tests: [paste or summarize]Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.
Use this to create a reviewable selection, not to replace your team’s release criteria or risk judgment. The PractiTest guide covers risk assessment and regression-test selection among its suggested testing tasks.
Plan performance testing
For [service or operation] and the workload assumptions below, propose load, stress, scalability, and resource-utilization scenarios. Separate measured requirements already provided from proposed targets. Ask for missing service-level objectives rather than inventing threshold values.
Workload and system context: [paste]
Known service-level objectives: [paste, or say none provided]
A prompt can organize test dimensions, but it cannot establish universal performance thresholds. Supply targets from your system requirements or applicable service-level objectives; treat any proposed target as a question for the team to approve.
Test UI flows and write actionable bug reports
Test [application and build] in [local, staging, or other named environment]. Exercise [priority user flows] using [relevant account state, data, and flags]. Focus on [functional, UI, copy, or regression issues]. For every issue report reproduction steps, expected result, actual result, severity, and environment; continue through the remaining flows unless a blocking issue should stop the run. End with a concise triage summary.
Specify account state, test data, feature flags, and the issue types that matter so findings have enough context to reproduce. OpenAI’s Computer Use QA use case recommends explicitly stating environment and flows, and requesting reproduction steps, expected and actual results, severity, and a summary.
Review requirements coverage
Compare the requirements below with the test inventory. Create a mapping of requirement to covering tests, identify requirements with no coverage and tests with unclear traceability, and suggest candidate additions. Distinguish confirmed gaps from possible gaps caused by missing context.
Requirements: [paste]
Test inventory: [paste]
Confirm each alleged gap against the complete requirement set and test inventory. Missing context can make a covered requirement look uncovered, or obscure a real gap.
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Use a short refinement cycle instead of accepting the first response:
- Check traceability. Ask which requirement or acceptance criterion supports each expected result.
- Separate assumptions. Request a list of unclear or absent rules instead of letting the model fill them in.
- Ask for missing dimensions. If the draft omits relevant roles, states, boundary values, or failure conditions, name those explicitly and request a revision.
- Validate the artifact. Review scenarios with product and engineering knowledge; run generated code in the actual project and inspect its setup and assertions.
- Iterate with focused feedback. Point to a specific omission or contradiction and ask for a correction, rather than asking vaguely for a better answer.
OpenAI’s guidance recommends clear context and iterative refinement. Neither a detailed prompt nor a polished format guarantees correctness or exhaustiveness.
What evidence says about generated test quality
A 2024 study of five software requirements specifications reported that about 87% of generated test cases were valid, 13% were inapplicable or redundant, and 15% of the valid cases had not previously been considered by developers. These are results reported by the study’s authors for that small dataset, not a general success rate; they explicitly caution that the dataset may not generalize. Read the study for its scope and qualifications.
A separate 2023 metamorphic-testing experience report found that most generated relation candidates were vague or incorrect, while some useful candidates emerged after domain experts evaluated them. That report is a reason to review unusual test ideas with subject-matter expertise, not a universal failure rate. See Luu, Liu, and Chen’s report.
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