Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Advance your QA practice by learning to strategically plan, execute, and measure testing efforts with precision. In this course, you will transition from executing tests to designing rigorous, risk-based test strategies that ensure comprehensive software coverage. You will build strong skills in creating high-quality test cases, managing test data provisioning, and optimizing existing test suites to eliminate redundancy and improve efficiency. Beyond execution, you will gain proficiency in data-driven quality management—calculating critical metrics such as pass-rates and defect density, and visualizing insights through professional dashboards. You will also apply Root Cause Analysis frameworks to identify and eliminate recurring defect patterns, delivering actionable, data-backed reports to stakeholders. Tools used include Zephyr (free 30day trial test management plugin) and Microsoft Copilot (accessible via free Microsoft account).

Syllabus

Requirements Analysis and RTM

Learn how to break down a user story into testable scenarios. You will use a Requirement Traceability Matrix (RTM) to ensure every requirement has corresponding test coverage and no feature is left untested.

Writing High-Quality Test Cases

A test scenario is just an idea; a test case is the exact recipe. Learn the industry standards for authoring clear, reproducible test cases inside Jira using Zephyr.

Provisioning Test Data

Tests cannot run without data. Learn how to provision valid and invalid datasets—such as test user accounts and edge-case inputs—to ensure your test cases are executable.

AI for Test Case Generation

Learn how to treat an AI like a Junior QA assistant. You will craft precise prompts to transform vague user stories into structured Gherkin (BDD) scenarios and step-by-step test cases.

AI for Synthetic Test Data

Generating test data manually is slow; using real user data is a privacy violation. Learn how to use GenAI to instantly produce massive, realistic, and privacy-compliant synthetic datasets.

Test Execution and Summary Reports

Learn how to officially "sign off" on a release. You will navigate completed test cycles in Zephyr and synthesize that raw data into a digestible Test Summary Report for your QA Lead and Product Owner.

Suite Optimization and Refactoring

Test suites grow over time, leading to bloated, slow execution cycles. Learn how to audit an existing regression suite in Jira/Zephyr to identify and merge redundant test cases. This maintenance activity, done during downtime, can save the team hours of effort.

The Core QA Metrics

Learn the fundamental mathematics of QA. You will understand how to calculate exactly how much of a feature is passing, how much is actually covered by tests, and how buggy a specific module is (Defect Density).

Visualizing Quality

Data is useless if no one can read it. Learn how to transform your raw metrics into a clean, professional dashboard using Microsoft Excel PivotCharts so you can clearly communicate feature health during Sprint Reviews.

The 5-Whys and DevTools Investigation

Learn the famous 5-Whys methodology. You will practice digging past the superficial UI symptom of a bug by opening your browser's DevTools to find the true technical failure underneath.

Corrective Actions and RCA Documentation

Once you find the root cause, you must communicate it. Learn how to translate your technical 5-Whys investigation into a formal Root Cause Analysis (RCA) document and open actionable improvement tickets in Jira.

Project Module: Test Design & Quality Dashboards

Synthesize your advanced planning and analytical skills as a Junior QA Tester. In this project, you will use Generative AI to design test cases and synthetic data for a new feature. You will upload and execute those tests in Zephyr, investigate a seeded technical failure using browser DevTools, and present your findings via a Root Cause Analysis and an Excel metrics dashboard.