Your cart is empty.
Keep shopping
Data-Driven Quality Assurance & Quality Control: Metrics/KPI
10,720 students
Data-Driven Quality Assurance & Quality Control: Metrics/KPI
Explore QA & QC Metrics & KPIs, defect trends, automation & manual testing KPIs, and quality measurement strategies
Created byAndrii Piatakha
Last updated 11/2025
English
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
Explore related topics
Course content
5 sections • 16 lectures • 5h 9m total length
Requirements
- Basic familiarity with how software testing works
- Knowledge of manual or automated quality assurance methods
- Experience using issue tracking tools like Jira or equivalent
- Hands-on use of tools that manage test cases, such as TestRail
- Motivation to apply metrics for QA improvements
- No specialized skills in programming or analytics required
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.
Who this course is for:
- Manual QA professionals aiming to showcase their impact through data
- Automation testers who need to quantify framework efficiency and consistency
- QA supervisors and team leads looking to apply measurable quality standards
- Agile testing specialists focused on integrating metrics into fast delivery environments
- Product owners and business analysts wanting actionable insights from QA metrics
- Software engineers interested in understanding how QA data can improve development
- Project coordinators managing delivery timelines and quality expectations
- Delivery leads responsible for monitoring release stability and defect rates
- Engineering managers using metrics to evaluate team and process performance
- Product managers aligning quality insights with product objectives and roadmaps
- System architects examining how architecture influences software quality and issue trends