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

Explore QA & QC Metrics & KPIs, defect trends, automation & manual testing KPIs, and quality measurement strategies

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

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 QualityIn 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 qualityAutomation & Manual Testing KPIs: Learn metrics for both types of testing—execution rates, pass/fail ratios, flakiness, automation coverageDefect Metrics & Trends: Discover how to use data to identify patterns, root causes, and quality risksQuality Measurement Strategies: Apply frameworks for tracking test coverage, product readiness, test case effectiveness, and moreProcess Improvement Through Metrics: Use historical data to drive retrospectives, reduce technical debt, and optimize test cyclesQA Dashboards & Reporting Techniques: Learn new things that will help you to build compelling, visual summaries using tools like Jira, Excel, or TestRailYou’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 visibleAutomation Testers looking to quantify their frameworks’ effectivenessQA Leads & Managers seeking to implement or improve their team’s quality metricsScrum Masters & Product Owners who want real-time insights into product and process qualityAnyone involved in software quality and delivery who wants to speak the language of dataWhy Metrics MatterIn 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.

Overview

Section 1: Introduction

Lecture 1 Communication plan

Lecture 2 Tips to Improve Your Course Taking Experience

Section 2: Defect Management Metrics & KPIs

Lecture 3 Defect Management Metrics & KPIs: Number of Open Defects & Defect Leakage

Lecture 4 Defect Management Metrics & KPIs: Defects per Severity/Priority/Env/Root cause

Lecture 5 Defect Management Metrics & KPIs: Defect Density, Non-Resolved Blockers & Others

Lecture 6 Defect Management Metrics & KPIs: Quality Debt Index, Bug Fixing Projection

Lecture 7 Defect Reopen, Defect Rejection, Open Defects Change Rate, Removal Efficiency

Lecture 8 Defect Resolution Time, Defect Age, Detection to Resolution Ratio

Section 3: Testing Metrics & KPIs

Lecture 9 Test execution coverage, Cost of Quality & Test Design Coverage

Lecture 10 Testing Metrics & KPIs: Regression Time, Verified Issues Rate, Pass Rate

Section 4: Test Automation Metrics & KPIs

Lecture 11 % of Poduct, Automation, System Issues & Execution Frequency

Lecture 12 Execution Time, Test Success Ratio & % of Results Analyzed

Lecture 13 Regression Effectiveness, Percentage of Automated Tests & Auto Savings

Section 5: Bonus Section

Lecture 14 Bonus Lesson

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