Data-Driven Quality Assurance & Quality Control: Metrics/KPI

Name: Data-Driven Quality Assurance & Quality Control: Metrics/KPI
Availability: InStock
Rating: 4.50 (925 reviews)

4.50

9,254 students

5h 9m

Updated Feb 2026

What you'll learn

Monitoring and analyzing the progress of test case execution

Creating actionable insights from defect trends

Spotting inefficiencies or slowdowns in QA processes

Measuring defect concentration and how often bugs escape to production

Identifying gaps in test scenarios using metrics

Estimating the return on investment from test automation efforts

Using metric-driven approaches to improve test planning

Combining manual and automated metrics

Measuring productivity of QA teams over time

Set QA & QC KPIs and tailoring them to project needs

Using test metrics to support compliance and audits

Using metrics to evaluate quality level on a project

Quantifying the cost of poor quality (CoPQ)

Building metric-based QA OKRs for teams

Using metrics to support root cause analysis sessions

Differentiating between bug severity and priority for better triaging

Designing reports that clearly communicate QA results to stakeholders

Using data during retrospectives to improve QA strategies

How to identify and define useful QA indicators and performance metrics

Evaluating how much of the system is tested and how effective the tests are

Course Description

Build a Metrics-Driven QA Practice with Confidence – Learn to Measure, Improve, and Communicate Software Quality

In modern software development, data is power — and that includes Quality Assurance. Whether you're testing manually, leading automation, or managing QA teams, the ability to collect and interpret the right QA metrics is what separates guesswork from strategy.

"Data-Driven Quality Assurance & Quality Control: QA Metrics" is a complete, practical guide to understanding and applying the most critical metrics in QA and QC. You’ll learn how to identify key trends, track testing performance, and present your results in a way that makes sense to both technical and non-technical stakeholders.

What This Course Covers:

Core QA & QC Metrics and KPIs: Understand the key differences and how both play a role in measuring quality
Automation & Manual Testing KPIs: Learn metrics for both types of testing—execution rates, pass/fail ratios, flakiness, automation coverage
Defect Metrics & Trends: Discover how to use data to identify patterns, root causes, and quality risks
Quality Measurement Strategies: Apply frameworks for tracking test coverage, product readiness, test case effectiveness, and more
Process Improvement Through Metrics: Use historical data to drive retrospectives, reduce technical debt, and optimize test cycles
QA Dashboards & Reporting Techniques: Learn new things that will help you to build compelling, visual summaries using tools like Jira, Excel, or TestRail

You’ll also get actionable tools: KPI templates, metric dashboards, formulas, and checklists you can use in real-world projects.

Who Is This Course For?

This course is ideal for:

QA Engineers & Testers aiming to make their work more measurable and visible
Automation Testers looking to quantify their frameworks’ effectiveness
QA Leads & Managers seeking to implement or improve their team’s quality metrics
Scrum Masters & Product Owners who want real-time insights into product and process quality
Anyone involved in software quality and delivery who wants to speak the language of data

Why Metrics Matter

In Agile and DevOps environments, decisions are made fast—and without data, QA can get left behind. This course teaches you how to bring clarity and credibility to your testing efforts. With real metrics, you can show exactly what’s working, what needs fixing, and how to prioritize your team's time effectively.

By the end of this course, you’ll be confident in building and using a QA metrics framework that drives real improvement—and gets noticed by your team, stakeholders, and leadership.

Join now and start delivering quality that’s not just good—but measurable.