Selenium with Python Frameworks : Data Driven & BDD

Learn how pytest, a Python testing framework, lets you organize and conditionally run multiple tests, set up prerequisites and post scripts, use assertions, and generate reports.

Learn naming conventions for pytest tests: name the file starting with test and the test method starting with test. Then run a selenium chrome test to validate a registration workflow.

Execute selenium tests with python frameworks conditionally by skipping tests using a skip decorator and skip if conditions, and run targeted tests with the -k keyword filter in pytest.

Group test cases to run targeted smoke and sanity tests within the data driven and BDD Selenium with Python framework, tagging and selecting or excluding groups with verbose execution.

Learn how to use fixtures in selenium with python to run pre and post test actions. Manage browser setup and teardown, and control scope with yield and global drivers.

Learn how to use assertions in selenium with python to compare actual results with expected values, verify browser titles and forms, and handle pass and failure cases in test scenarios.

Convert the Selenium test to pytest format by renaming the file, wrapping code in a test function, and using fixtures to manage the browser, then assert actual versus expected results.

Create a project structure with folders for test cases, base code, libraries, configuration data, pages, and assertions; implement a page object model and driver files for maintenance in one place.

Organize a Selenium with Python project by creating a base driver module, start and close browser helpers, and import the shared driver across test case files for data-driven testing.

Learn to drive tests with data from a config file by reading details like browser name and driver paths, and switch between Chrome and Firefox using a reusable config reader.

Move element locators to a separate config file and read them with a config reader, centralizing username, email, and password locators for easier maintenance across tests.

Master the page object model design pattern in Selenium with Python, creating a page class for each page and encapsulating actions like enter username and sign in to improve maintenance.

Demonstrates implementing the page object model in Selenium with Python by creating a registration page class with methods for username, password, email, and dropdowns, driven by a driver.

Demonstrate data driven testing in selenium with python by driving tests with multiple data sets from excel or csv, including positive and negative data.

Fetch test data from an excel sheet using openpyxl, read usernames and passwords from sheet1, and drive multiple test iterations with a data driven approach.

Explore the basics of behavior driven development, defining end-to-end application behavior in a BDD approach with Gherkin and step definitions to automate tests.

Discover the common files in a BDD framework for Selenium testing: feature files in Gherkin, step definition code, and a test runner to execute and generate reports.

Learn to write feature files in Gherkin for behavior-driven testing, define end-to-end scenarios, and link them to step definitions in Cucumber. Discover Gherkin keywords, .feature extension, and setup with Notepad++.

Use the Gherkin scenario keyword to define test cases under a feature, with multiple scenarios like registered user search item, add to cart, and purchase.

Explore behavior-driven development by outlining test cases as scenarios with given, when, and then steps, starting from the login page and detailing actions.

Learn Gherkin basics by using when, then, and, and end to model multi-action steps and validations in a test case scenario.

Learn how Gherkin's background step consolidates common given steps across scenarios, moving repeated setup from each scenario into a background that runs before every scenario in a feature.

Explore how scenario outline enables data driven testing by running the same scenario with multiple data sets defined in the examples data table, using parameters and step arguments.

Explore Gherkin comments and doc strings to pass large test data in bdd; use hash for comments and triple-quoted strings as last arguments.

Apply tags to feature, scenario, and scenario outline to control execution. Leverage how tags are inherited from feature to scenarios, outlines, and examples for smoke, sanity, and regression tests.

Create a robust BDD automation project structure using behave: install behave, organize features, steps, and environment files, and establish a test folder with feature and step definitions.

Create a feature file with given when then steps for registration, including valid and duplicate-name scenarios, in the features folder, to drive automated test code.

Map feature file steps to Python behave step definitions and implement Selenium automation, driving Chrome, locating elements, and executing data-driven bdd scenarios.

Learn to use tags in BDD to control execution, with smoke and sanity as examples, and apply or and logic to filter test runs.

Explore how the environment file uses before all, after all, before feature, after feature, and before scenario hooks to manage a browser driver via context in Selenium with Python.

Learn how to generate formatted reports in a Python Behave BDD framework by installing Behave, configuring report formatters, producing an XML report, and viewing the HTML report in a browser.

Install and import the openpyxl package, load a workbook, list sheets, and access the active sheet title to begin reading data from Excel.

Read one cell data from an Excel sheet in a Python data-driven Selenium workflow, moving from workbook to sheet to cell and fetching values by row and column.

This lecture shows how to read all data from an Excel sheet in a data driven Selenium workflow. Count rows and columns, iterate cells, and fetch values using looping approaches.

Learn to write data to an Excel file by creating a workbook, renaming sheets, and writing cell values. Save changes and manage sheets, including creating a second sheet.

Learn the basics of JSON syntax, including objects defined by curly braces, key-value pairs, and arrays for multiple values, enabling data transfer and schema design for automation.

Master json path basics to navigate json data and validate specific fields in a response, such as city, phone number, or last name, using path expressions.

Learn advanced json path techniques to navigate nested json objects using dot notation and indices, fetch city, house number, and mobile, and handle complex data structures.

Parse a Python dictionary to JSON and back using the json module, converting between string and dictionary formats with sample data and printed results.

learn how to perform api testing with python by sending http requests, parsing json responses with jsonpath, validating status codes, and extracting fields like total and first names from data.