Tutorials > Data-Driven Quality Assurance & Quality Control: Metrics/kpi

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

Date: 1 June 2025 | Posted by: 0nelove

Data-Driven Quality Assurance & Quality Control: Metrics/kpi
Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.61 GB | Duration: 4h 49m
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

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