Can LLMs generate Enterprise Quality Code?
summary
Sonar runs 4,444 Java assignments through 53 models and reports concrete metrics: GPT‑5.4: 1.2M LOC, GPT‑4o: <250k LOC, pass rates, cyclomatic and cognitive complexity, bug density and security issues per million lines. Claude Sonnet 4.6 shows 627k LOC and 300 security issues/MLOC. The workflow is the ACDC framework: ACDC: guide verify solve, with SonarQube agentic analysis taking about 1–5 seconds before commit and a remediation agent that creates one fix per issue, compiles and re‑analyzes before offering the PR.
AI-written summary · about
chapters (12)
- 0:00Introduction and the Shift to Agentic Development
- 1:44Evaluating LLM Code Quality and Reliability
- 3:00Sonar's Evaluation Framework and Methodology
- 3:39LLM Performance Analysis (Pass Rates and Code Bloat)
- 5:24Why LLMs Struggle: Training Data and Hidden Flaws
- 6:45The Sonar LLM Leaderboard
- 8:30Complexity Metrics: Cyclomatic vs. Cognitive
- 10:41The ACDC Framework: Guide, Verify, and Solve
- 11:06Phase 1: Guide (Context Augmentation & Sonar Sweep)
- 11:42Phase 2: Verify (SonarQube Agentic Analysis)
- 12:40Phase 3: Solve (Remediation Agent)
- 14:05Product Summary and Ecosystem Support
related talks
16mThe LLM Leaderboard: Benchmarking AI Coding Models
46mIn search of Clean Code with Sonar, 20,000 leagues under an ocean of code!
25mEvaluating An LLM's Ability To Code
15mSonar-Powered LLM Context Augmentation for AI Coding Agents
16mHow to Verify AI-Generated Code with AI Coding Agents
47mValidating LLMs – A True Story
22mCode Security in the AI Era | SAST, SCA & Secrets Detection
14mWhich Code Issues Actually Matter? | Prioritizing SonarQube Findings