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Explore Can LLMs generate Enterprise Quality Code? — Prasenjit Sarkar, Sonar with 7,560 views, 159 likes, and 25 comments. Experience the impact of this video content that has captured audience attention.

Sonar ran 4,444 Java programming assignments through 53 models and measured what actually came out. GPT-4o generated under 250,000 lines for those assignments. GPT 5.4 generated 1.2 million. Claude Sonnet 4.6 generated 627,000 with the highest security issue rate at 300 per million lines of code. Prasenjit Sarkar from Sonar walks through the full leaderboard: pass rate, cyclomatic complexity, bug density, and security issues per model. Their response is a three-stage framework called ACDC: guide, verify, solve. The verify stage runs SonarQube analysis in 1 to 5 seconds before a commit, against 1 to 5 minutes in CI. If issues slip through to the PR, a remediation agent creates one fix per issue, runs it through analysis and compilation to check for regressions, and only presents it if it passes. Speaker info: - https://www.linkedin.com/in/jit2600/ Timestamps: 0:00 Introduction and the Shift to Agentic Development 1:44 Evaluating LLM Code Quality and Reliability 3:00 Sonar's Evaluation Framework and Methodology 3:39 LLM Performance Analysis (Pass Rates and Code Bloat) 5:24 Why LLMs Struggle: Training Data and Hidden Flaws 6:45 The Sonar LLM Leaderboard 8:30 Complexity Metrics: Cyclomatic vs. Cognitive 10:41 The ACDC Framework: Guide, Verify, and Solve 11:06 Phase 1: Guide (Context Augmentation & Sonar Sweep) 11:42 Phase 2: Verify (SonarQube Agentic Analysis) 12:40 Phase 3: Solve (Remediation Agent) 14:05 Product Summary and Ecosystem Support

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