Master Quality Assurance & Quality Control in Coding — Real-World Scenarios & AI AI Tools for Modern Development
What you'll learn
Introduction to QAQC and Its Importance
QAQC For Software Development
Practical Gen AI Concepts
QAQC AI Tools
Requirements
Prior exposure to AI
Description
This course contains the use of artificial intelligence.
Software quality is not accidental — it's engineered.
In today's fast-paced development environments, writing code is only part of the job. Ensuring that code is reliable, secure, scalable, and production-ready is what separates amateur projects from professional systems.
This course teaches you how to implement Quality Assurance (QA) and Quality Control (QC) practices specifically for software development — using modern workflows and AI-powered tools.
Whether you're building a startup web app or developing a high-risk fintech system, QAQC is essential to prevent bugs, reduce security risks, and deliver software with confidence.
What You'll Learn
By the end of this course, you will
• Understand the difference between Quality Assurance and Quality Control in coding
• Design QAQC processes for real-world software projects
• Implement testing strategies, including unit, integration, regression, and security testing
• Apply code review and pull request best practices
• Use AI tools like GitHub Copilot for early-stage quality support
• Use static analysis tools to detect vulnerabilities and code smells
• Build CI/CD pipelines that enforce automated quality checks
• Scale QAQC practices based on project risk level
• Reduce bugs in production and improve deployment confidence
Tools & Technologies Covered
• GitHub Copilot
• Static analysis tools (e.g., Snyk, SonarQube concepts)
• Automated testing frameworks
• CI/CD quality gates
• Secure coding practices
• AI-assisted test generation
No advanced AI knowledge required — everything is explained clearly and practically.
Who this course is for
Programming students
Coders
Developers
Project managers