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SELENIUM + AI — FROM PROFESSIONAL FRAMEWORKS TO AI-POWERED AUTOMATION

Advanced Selenium, Frameworks &
AI-Powered Testing

Build on your Python and Selenium foundation. Master professional frameworks, CI/CD pipelines, BDD, and Selenium Grid — then add AI tools that make you one of the most capable automation engineers in the market.

40+
Days of Training
12
Modules
100+
Topics Covered
AI
Powered

Who Is This Course For?

This course is designed for testers and developers who already have basic Python and Selenium experience. You will move from individual scripts to production-grade frameworks, then layer in AI capabilities — covering pytest, Selenium Grid, Jenkins, BDD, and Gemini API from first principles to hands-on projects.

QA Engineers Python Developers Automation Testers CI/CD Learners Framework Architects AI-Curious Testers

Revision Classes — Python Advanced OOP & Selenium Advanced

The first few sessions of each batch cover a hands-on revision of Python Advanced OOP (strings, collections, *args/**kwargs, the four OOP pillars, exception handling) and Selenium Advanced techniques (XPath axes, ActionChains, Select, alerts, iFrames, Shadow DOM). Depth and pace are calibrated to each batch's existing level — these are not fixed-day modules.

Your Learning Journey

Python OOP + Selenium Revision
Grid, Docker & Jenkins
BDD + Hybrid Framework
Gemini API + n8n Workflows
AI Agents + Healenium
MODULE 01
Synchronisation, SVG & File Handling
Timing strategies, data files, flakiness prevention

Flaky tests are almost always a timing or data problem. This module shows you how to wait correctly, interact with SVG-based elements, and pull test data from the file formats you'll encounter in real projects.

Core Topics
  • Explicit waits with WebDriverWait + expected_conditions: visibility_of_element_located, element_to_be_clickable
  • SERE pattern: Scroll → Existence check → Readiness check → Element action
  • SVG element interaction: locate <svg>, <path>, <circle> nodes via XPath wildcards
  • CSV handling with csv.reader and Pandas read_csv() — parameterised test data
  • Excel I/O with openpyxl: read rows, write results, multi-sheet access
  • pytest-rerunfailures: automatic retry for flaky network or rendering issues
WHY THIS MODULE

Flakiness is the number-one reason test suites lose the trust of development teams. Using time.sleep() everywhere is the most common beginner mistake — it either wastes time when the page loads fast or still fails when it loads slowly. The SERE pattern and proper WebDriverWait conditions make your tests both faster and more reliable at the same time.

MODULE 02
Config, JSON & SQL Data Sources
Stop hard-coding — drive your tests from real data sources

Hard-coded values in test scripts are a maintainability nightmare. This module shows you how to externalise your configuration, load test data from JSON, and query databases directly.

Core Topics
  • Config management with configparser: read .ini / .cfg files for environment-specific settings
  • JSON test data: json.load(), nested object traversal, writing results back to JSON
  • Database integration: mysql-connector-python, parameterised queries with cursor.execute()
  • Pulling test data directly from a database into @pytest.mark.parametrize
  • Environment switching (dev/staging/prod) without editing test files
WHY THIS MODULE

When your app's test database has 500 user records, you don't want to copy-paste them into your test file — you want to query them directly. JSON and SQL data sources make your tests data-driven by design: add a new row to the database and the test automatically covers a new case. This is how enterprise QA teams manage test coverage at scale.

MODULE 03
Logging, pytest-html & Allure Reports
Turn test results into professional evidence that managers trust

A test suite that runs silently and dumps a wall of terminal output is hard to trust. This module teaches you to produce structured logs and beautiful reports that clearly show what passed, what failed, and exactly why.

Core Topics
  • Python logging module: DEBUG, INFO, WARNING, ERROR levels; FileHandler + StreamHandler
  • Rotating log files with RotatingFileHandler — prevent logs growing unbounded
  • pytest-html: one-command HTML reports with --html=report.html --self-contained-html
  • Allure framework: @allure.step, @allure.description, @allure.severity, allure.attach()
  • Auto-attach screenshots on test failure using pytest hooks and allure.attach()
  • Video recording with ffmpeg attached to Allure report on failure
WHY THIS MODULE

Allure reports are what you show in the demo round of a job interview. When a recruiter asks "how do you report test results to stakeholders?", showing an Allure dashboard with pass-rate trends, step-by-step breakdowns, and auto-attached failure screenshots is a completely different answer than "I look at the pytest output." Reports are also how your team trusts the suite — without them, nobody knows whether a failure is a real bug or a flaky test.

MODULE 04
Selenium Grid & Docker
Run tests across browsers and machines simultaneously

Running a large suite sequentially on one machine takes too long. Selenium Grid distributes your tests across multiple browsers and machines — locally with Docker, or in the cloud with SauceLabs and BrowserStack.

Core Topics
  • Selenium Grid architectures: Standalone, Hub-Node, and fully Distributed modes
  • Grid setup with selenium-server.jar: --role hub, --role node, capability matching
  • Docker Compose for Grid: selenium/hub, selenium/node-chrome, selenium/node-firefox images
  • Connecting to RemoteWebDriver with browser Options()
  • SauceLabs and BrowserStack integration: tunnel setup, result tagging, session replay
  • Parallel execution with pytest-xdist: -n auto flag, fixture scope considerations
WHY THIS MODULE

Cross-browser testing is on almost every job description — but most people mean "I ran it on Chrome." Real cross-browser testing means running on Chrome, Firefox, and Edge in the same CI run. Selenium Grid makes that possible, and Docker makes it reproducible: any machine with Docker installed can spin up your exact Grid environment in 30 seconds — no manual browser driver setup required.

MODULE 05
Jenkins CI/CD Pipeline
Automate your test runs — trigger on every code push

Manual test runs that depend on a person clicking "Run" are not CI/CD. This module teaches you to build a Jenkins pipeline that triggers automatically on every push to GitHub — and sends email alerts when tests fail.

Core Topics
  • Jenkins installation, initial setup, and plugin management (Git, Allure, Email Extension)
  • Connecting Jenkins to a private GitHub repository with SSH keys or tokens
  • Creating a Freestyle job and a Pipeline job from scratch
  • Writing a Jenkinsfile: pipeline, stages, steps, post blocks
  • GitHub webhook setup: auto-trigger Jenkins build on git push
  • Scheduled builds using CRON syntax and email notification on failure
WHY THIS MODULE

A framework that only runs when someone remembers to run it is not automation — it's manual work with extra steps. Jenkins is how professional teams ensure tests run on every single code change, providing instant feedback. When you can show a working Jenkins pipeline with auto-trigger and email alerts in an interview, you demonstrate that you understand the full software development lifecycle — not just test scripting.

MODULE 06
Keyword-Driven Framework
Let non-programmers write test cases using plain action words

A keyword-driven framework separates test design from test code. Test cases live in Excel — written with keywords like click, enter_text, verify_text — while your Python engine reads and executes them.

Core Topics
  • Keyword architecture: Excel-based test cases → Python keyword dispatcher → Selenium actions
  • Implementing a keyword library: click_element(), enter_text(), verify_text(), navigate_to()
  • Reading test cases from Excel with openpyxl and iterating rows as test steps
  • Reporting pass/fail results back into the Excel file per test step
  • Extending the keyword library without modifying existing test cases
WHY THIS MODULE

In large QA teams, manual testers write the test cases — automation engineers build the engine that runs them. A keyword-driven design makes this collaboration clean: the manual tester uses Excel, the automation engineer adds Python keywords when new actions are needed. Neither has to understand the other's work in detail. This is a common architecture in enterprise insurance, banking, and ERP test teams.

MODULE 07
BDD with pytest-bdd
Write test scenarios in Gherkin — the language business understands

Behaviour-Driven Development bridges the gap between business requirements and automated tests. Your .feature files are living documentation — readable by product managers, developers, and QA — while your step definitions are the actual test code.

Core Topics
  • Gherkin syntax: Feature, Scenario, Given, When, Then, And, But
  • pytest-bdd step definitions with @given, @when, @then decorators
  • Scenario Outline + Examples table — data-driven BDD scenarios
  • Shared fixtures and browser setup in conftest.py — scope management
  • Allure integration with BDD: @allure.feature, @allure.story, step-level reporting
  • Video capture on failing scenarios with auto-attach to Allure
WHY THIS MODULE

BDD is where QA and product teams actually collaborate. When a product manager writes acceptance criteria as Gherkin scenarios, and your automated tests run those exact scenarios, you've eliminated the guesswork about whether the software meets requirements. pytest-bdd is the most Pythonic way to implement this — and when combined with Allure, every failing scenario comes with a human-readable story and a screenshot.

MODULE 08
Hybrid Framework
Combine POM, data-driven, and BDD into one complete architecture

The Hybrid Framework is the culmination of everything you've built. It combines Page Object Model, fixture-based test management, parallel execution, and Allure reporting into a single, maintainable codebase — the architecture most product-company QA teams use.

Core Topics
  • Hybrid framework structure: pages/, tests/, data/, reports/, utilities/, conftest.py
  • Page Object Model: one class per page, locators and actions together, inheritance from BasePage
  • conftest.py scoping: session-scoped driver fixture, function-scoped data fixtures
  • Parallel test execution with pytest-xdist — -n 4 workers
  • Full Allure report generation: allure generate --clean pipeline command
  • Grid mode toggle: GRID=yes/no in config.properties — switch between local and remote
WHY THIS MODULE

When someone asks "what does your framework look like?" in an interview, you want to answer with a folder structure, not a description. The Hybrid Framework you build here is the tangible proof of your skills — a working codebase that demonstrates Page Objects, parallel execution, data parameterisation, and Allure reports all in one project. It's the centrepiece of your portfolio.

MODULE 09
AI Fundamentals & Gemini API
Understand AI from scratch — then build with it

You don't need to be a data scientist to use AI in your work. This module gives you enough understanding to work confidently with AI models, then gets you building real tools using Google's Gemini API and a simple web interface.

Core Topics
  • ML fundamentals: supervised vs unsupervised learning, training/test split, overfitting — with scikit-learn examples
  • Generative AI concepts: LLM tokens, prompt engineering, temperature, context windows
  • Google Gemini API: authentication with API key, google-generativeai Python SDK, model selection
  • Building a test assistant chatbot: send prompts about your app and get test scenarios back
  • Streamlit web app: st.text_input(), st.button(), st.markdown() — deploy in 10 lines
  • AI Agent pattern: function_calling with Gemini — let the model decide which tool to invoke
WHY THIS MODULE

The automation engineer who can use AI to generate test scenarios, summarise test results, and build small internal tools is significantly more productive than one who can't. The Gemini API is free to start, straightforward to integrate with Python, and the Streamlit interface means you can turn an AI script into a shareable web tool in an afternoon. This is not a future skill — it is being used in QA teams right now.

MODULE 10
n8n Workflow Automation
Connect apps and move data automatically — without custom code

n8n is an open-source workflow automation tool that connects apps through a visual interface. Think of it as an automation layer above your test infrastructure — routing data between Google Forms, Sheets, AI models, and email without writing plumbing code.

Core Topics
  • n8n setup with Docker: docker run -p 5678:5678 n8nio/n8n, persistence with volumes
  • Building your first workflow: Google Form trigger → Google Sheets append node
  • Gemini AI node: send collected data to Gemini for analysis, summarisation, or classification
  • Gmail / SMTP node: auto-send email with AI-generated summary on form submission
  • Conditional branching: IF node to route workflows based on data values
  • Scheduling workflows: cron-based triggers for nightly data collection or report generation
WHY THIS MODULE

As an automation engineer, you'll often be asked to connect things that don't naturally talk to each other — "Can we get a Slack message when the nightly test run fails?" or "Can we auto-populate a spreadsheet with new bug reports?" n8n makes these connections visual and maintainable. Paired with Gemini AI, it can also add intelligence to those workflows — summarising content, categorising data, or drafting responses automatically.

MODULE 11
AI in Testing & Vibe Coding
Use AI as a co-pilot across your entire testing workflow

AI is changing how automation code is written. This module teaches you to use AI to generate test scenarios, write Playwright scripts, and create agents that can browse the web on your behalf — including setting up Playwright MCP so your AI assistant has direct browser access.

Core Topics
  • AI-assisted test scenario generation: prompt engineering for Given/When/Then output
  • Vibe coding workflow: describe the automation goal in natural language → review AI-generated Playwright code → refine iteratively
  • Playwright MCP (Model Context Protocol): connect Claude/Gemini to a live browser instance
  • Agentic AI in testing: multi-step agents that navigate, assert, and report using function_calling
  • Reviewing and validating AI-generated test code — spotting hallucinated selectors and wrong assertions
  • Integrating AI-generated scenarios into your existing pytest + Allure pipeline
WHY THIS MODULE

Vibe coding is not about replacing your skills — it's about multiplying your output. An engineer who can describe a test scenario in plain English and have an AI produce a working Playwright script in seconds still needs to understand what the script is doing and whether it's correct. This module teaches both sides: how to use AI for acceleration, and how to maintain the critical eye that ensures the output is actually right.

MODULE 12
smolagents & Healenium
Self-healing tests and lightweight AI agents for test automation

The final module combines two cutting-edge capabilities: Healenium makes your Selenium tests self-repairing when a website's structure changes, and smolagents gives you a lightweight framework for building autonomous AI agents that can act on your behalf.

Core Topics
  • Healenium architecture: proxy between WebDriver and browser — intercepts failed locator calls and recovers using ML similarity scoring
  • Healenium setup: Docker-based backend (healenium-backend + healenium-selector-imitator), HealingDriver wrapper
  • Self-healing in action: intentionally break a locator, watch Healenium find and heal it automatically
  • smolagents (Hugging Face): agent loop with CodeAgent, tool definition with @tool decorator
  • Building a test agent: tool set includes navigate(), click(), get_text(), assert_equals()
  • Agentic vs scripted automation — understanding when each approach is the right choice
WHY THIS MODULE

Test maintenance is the hidden cost of automation. Every time the front-end team renames a CSS class or moves a button, selectors break and someone has to update them. Healenium removes that burden for the majority of cosmetic changes — letting your suite stay green even as the UI evolves. Combined with smolagents, you're building toward fully autonomous test execution: an agent that can observe a new page it has never seen before and still verify that the core functionality works correctly.

Interview Preparation

Selenium Grid types

Standalone runs a single node + hub together. Hub-Node separates them — one hub manages multiple nodes. Distributed mode adds Router, Distributor, and Session Map as separate services for maximum scalability. Docker Compose makes Hub-Node the easiest to spin up and tear down.

What is the SERE pattern?

Scroll → Existence → Readiness → Element action. Before interacting with any element, you scroll it into view, wait for it to exist in the DOM, confirm it is clickable or interactable, then perform the action. This sequence eliminates most flakiness caused by timing and visibility issues.

How does pytest-bdd work?

You write .feature files in Gherkin. Each Given, When, Then line maps to a Python function decorated with @given, @when, @then. pytest runs these as normal test cases, so your full pytest ecosystem — fixtures, markers, Allure, xdist — works exactly the same way.

How does Healenium heal tests?

Healenium records the DOM context around every element your tests successfully find. When a locator fails in a future run, it queries its backend for the most similar DOM structure — using a tree similarity algorithm — and returns the best matching element. It also suggests an updated locator so you can fix the source permanently.

What is MCP and why does it matter?

Model Context Protocol is an open standard for giving AI models access to external tools and data sources. Playwright MCP specifically gives a model like Claude or Gemini the ability to control a real browser — navigate, click, read page content, and take screenshots — making it a true agentic browser automation tool.

What makes an AI agent different?

A script follows a fixed sequence of steps. An agent has a goal and a set of tools — it reasons about what to do next, picks a tool, acts, observes the result, and decides the next step. In smolagents, this loop is explicit: CodeAgent generates Python code as actions, executes them, and continues until the goal is achieved.