What is the Difference Between Me and You? Benchmarking the Quality Gap Between Human-Written and AI-Generated Code

arXiv:2609.12708 · cs.SE, cs.AI · Submitted 2026-09-11 · Read on arXiv

cs.SE, cs.AI

Submitted: 2026-09-11

Updated: 2026-09-21

Comments: Preprint. This manuscript is currently under peer review

Code: https://github.com/github/codeql

Project page: https://pmd.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 51× in this paper · explained in 2 episodes
• Benchmarking — 28× in this paper · explained in 3 episodes
• Analyzer — 26× in this paper · explained in 1 episode
• Perplexity — 18× in this paper · explained in 3 episodes
• Clustering — 9× in this paper · explained in 2 episodes
• Scaffolding — 9× in this paper · explained in 3 episodes
• Orthogonal Defect Classification — 8× in this paper
• Common Weakness Enumeration — 7× in this paper
• Calibration — 5× in this paper · explained in 7 episodes
• Lexical Diversity — 5× in this paper · explained in 2 episodes
• Transferability — 5× in this paper · explained in 4 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Abstraction — 3× in this paper · explained in 1 episode
• Failure Modes — 3× in this paper · explained in 3 episodes
• Mining Software Repositories — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Generalization — 2× in this paper · explained in 13 episodes
• International Symposium on Software Reliability Engineering — 2× in this paper
• Interpretability — 2× in this paper · explained in 15 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Agentic Coding — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Program Synthesis with Large Language Models
• Is GPT-OSS Good? A Comprehensive Evaluation of OpenAI's Latest Open Source Models
• Evaluating Large Language Models Trained on Code
• FullStack Bench: Evaluating LLMs as Full Stack Coders
• DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
• Measuring Coding Challenge Competence With APPS
• Qwen2.5-Coder Technical Report
• CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
• Efficient Memory Management for Large Language Model Serving with PagedAttention
• Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens
• Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild
• A Large-Scale Comprehensive Measurement of AI-Generated Code in Real-World Repositories
• Investigating The Smells of LLM Generated Code
• Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis
• An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?
• Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness
• CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
• Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models
• A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends

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• PackMonitor: Enabling Zero Package Hallucinations Through Decoding-Time Monitoring
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