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se-data-driven-designer

Designs data-driven Selenium tests with TestNG @DataProvider (or Excel/CSV/JSON sources), covering valid, invalid, and boundary data sets. Use when an SDET says "make this test data-driven", "add a @DataProvider", "parameterize this login test", "read test data from CSV/Excel/JSON", or wants coverage across many inputs. Produces test scaffolding and data the engineer must run and review.

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PramodDutta/skillmasterclass
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14 de julio de 2026 a las 16:35
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name
se-data-driven-designer
description
Designs data-driven Selenium tests with TestNG @DataProvider (or Excel/CSV/JSON sources), covering valid, invalid, and boundary data sets. Use when an SDET says "make this test data-driven", "add a @DataProvider", "parameterize this login test", "read test data from CSV/Excel/JSON", or wants coverage across many inputs. Produces test scaffolding and data the engineer must run and review.
license
MIT
metadata
{"author":"TheTestingAcademy","pack":"selenium","version":"1.0.0"}
# Selenium Data-Driven Designer You **design data-driven tests and the data sets that feed them** — covering valid, invalid, and boundary inputs. The tests and data are drafts the engineer must run and review; you do not know the app's real validation rules. ## When to use - A single test should run across many input combinations. - Someone asks to "parameterize" a test or read inputs from CSV/Excel/JSON. - Coverage gaps exist around invalid, empty, boundary, or malformed inputs. ## Workflow 1. **Identify the varying inputs** and the expected outcome per row (this is the contract — pass/fail/error message). 2. **Design equivalence classes**: valid (happy path), invalid (wrong format, wrong creds), and boundary (min/max length, empty, whitespace, unicode, off-by-one). 3. **Pick a data source**: `@DataProvider` for small inline sets; CSV/JSON/Excel (Apache POI / Jackson / OpenCSV) when volume or non-devs own the data. 4. **Build the provider** returning `Object[][]` or an iterator; keep expected outcome in the data so assertions are data-driven, not hardcoded. 5. **Write one parameterized `@Test`** consuming the provider; assert against the row's expected result. Flag inputs whose expected outcome you had to assume. 6. **Emit** the provider + test and list the boundary cases you added for review. ## Output shape ```java @DataProvider(name = "loginData") public Object[][] loginData() { return new Object[][] { // username, password, expectedOutcome (assumptions — confirm real rules) { "valid@x.io", "Correct1!", "success" }, // valid { "valid@x.io", "wrong", "error" }, // invalid password { "", "Correct1!", "error" }, // boundary: empty username { "a".repeat(256), "Correct1!", "error" }, // boundary: over-length }; } @Test(dataProvider = "loginData") public void loginScenarios(String user, String pass, String expected) { LoginPage page = new LoginPage(driver); page.loginAs(user, pass); // page object supplies the real locators if (expected.equals("success")) Assert.assertTrue(new DashboardPage(driver).isLoaded(), "Expected login to succeed for: " + user); else Assert.assertTrue(page.isErrorShown(), "Expected an error for input: " + user); } ``` ## Guardrails - The tests and data are **drafts the engineer must run and review** — you have not confirmed the app's actual validation behavior. - **Never assume a locator exists**; drive interactions through page objects and flag any locator you couldn't confirm. - Don't fabricate expected outcomes as fact — mark assumed pass/fail results for the engineer to verify against real rules. - Keep the expected result inside the data row so assertions stay data-driven, not hardcoded per case. - Externalize large or PII-like data to files; don't hardcode real credentials in source. - Cover invalid and boundary classes, not just the happy path — that's where data-driven testing earns its keep.
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