Project write-ups

UI Regression Suite — SauceDemo (Swag Labs)

SauceDemo is Sauce Labs' e-commerce demo app, widely used as a stable, purpose-built target for practicing UI automation. This suite covers the full shopper journey: login (including locked-out and problem-user edge cases), product sort/filter, cart operations, and checkout — the four flows most likely to break silently after a front-end change.

Built keyword-driven in Robot Framework so test cases stay readable as plain-English steps, with a shared keyword library handling login, cart state, and checkout to keep individual test files short and easy to review in a pull request.

What it covers

• Login flows across all six SauceDemo user types (standard, locked-out, problem, performance-glitch, error, visual)
• Product sorting (name A–Z/Z–A, price low–high/high–low) with programmatic order verification
• Cart add/remove across single and multi-item flows, including badge count verification
• Full checkout: form validation, order summary math, and confirmation state

Notable catch

During development, the suite flagged that the "problem_user" flow silently swapped product images between items — caught by pixel-diff assertions before it could ship.

API Test Suite — Restful-Booker

Restful-Booker is a public REST API built specifically for practicing API test automation (booking creation, auth, and CRUD operations for a fictional hotel). This suite treats it like a production API: every response is validated against a JSON schema, not just a status code, and auth token handling is tested for both the happy path and expiry/invalid-token cases.

What it covers

• Full CRUD on bookings (create, read, update, partial update, delete) with schema validation on every response
• Auth token generation, reuse across a session, and rejection of expired/invalid tokens on protected endpoints
• Negative testing: malformed payloads, missing required fields, invalid date ranges
• Data-driven parametrization across a matrix of booking payloads

Why schema validation

Schema checks catch breaking changes — a field silently disappearing or changing type — that a simple status-code check would miss, and that are easy to ship and hard to spot manually.