Use ChatGPT to explore how software development and quality metrics can inform your program and project management decisions. You'll begin with metrics, key performance indicators, objectives and key results, and RAG status before examining engineering excellence measures. You'll analyze technical debt, cyclomatic complexity, unit test metrics, duplicate code, code review feedback time, rules compliance, and integration and end-to-end test coverage. You'll also examine build and pipeline timings, green and broken builds, release cycle time, and release candidate metrics. Your focus then shifts to quality assurance and control, including defect leakage, density, severity, resolution time, quality debt, and bug-fixing projections. You'll explore test execution and design coverage, regression time, pass rates, automation measures, and the cost of quality. Through these topics and module quizzes, you'll build your ability to use development and testing data with ChatGPT to inform release decisions.

ChatGPT for Software Development and QA Metrics
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ChatGPT for Software Development and QA Metrics
This course is part of ChatGPT for Program Management: From Basics to Advanced Specialization

Instructor: Andrii Piatakha
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Recommended experience
What you'll learn
Analyze development metrics, including technical debt, code complexity, test coverage, and code review feedback time.
Interpret build, pipeline, and release metrics alongside defect leakage, density, resolution time, and quality debt.
Use ChatGPT to explore testing, regression, and automation metrics as inputs to release decisions.
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November 2026
2 assignments
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