With great power comes great responsibility in code quality
summary
Generative AI accelerates coding—GitHub Copilot allegedly writes 46% of code and speeds work by up to 55%—but models still produce bugs, security flaws, and style problems: a study of ~2,000 tasks finds 53% of Java outputs that pass tests still have maintainability issues. Recommended setup pairs IDE checks (SonarLint in connected mode) with CI analysis (SonarQube or SonarCloud, quality gates, 5,000+ rules) so AI boosts velocity while automated scans enforce reliability, security, and reduced technical debt.
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chapters (5)
- 0:00AI and code quality responsibility
- 3:48AI coding assistants overview
- 8:26AI-generated code risks
- 13:08Sonar with AI tools
- 17:27Responsible AI code adoption
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