AI bug detection identifies potential bugs, errors, and vulnerabilities in code before they cause problems. What it finds: Logic errors, null pointer issues, resource leaks, off-by-one errors, security vulnerabilities, concurrency bugs, type mismatches. Approaches: Static analysis: Analyze code without execution, pattern matching, data flow analysis. ML-based: Models trained on bug-fix pairs, learn patterns that indicate bugs. LLM review: Language models analyze code for issues using learned code understanding. Tools: SonarQube (rules-based), DeepCode/Snyk Code (ML-based), CodeQL (query-based), Semgrep (pattern matching), LLM-based reviewers. Security scanning: SAST (static application security testing), specialized for CVE patterns, OWASP vulnerabilities. IDE integration: Real-time feedback as you type, inline warnings, suggested fixes. False positive challenge: Balancing sensitivity (catch bugs) vs precision (avoid noise). LLM limitations: May miss subtle bugs, hallucinate bugs, less reliable than formal methods. Best practices: Layer multiple tools, tune sensitivity, prioritize by severity, integrate into CI/CD. Complement to testing.