Paper: Vibe coding: programming through conversation with artificial intelligence. 2025 1 What do they do, and what’s the result? 1.1 What do they do? 1.1.1 1.1.2 1.1.3 1.2 What’s the result? 1.2.1 1.2.2 1.2.3 2: Can I use any of it in my work? If yes, how? 2.1 … 2.1.1 2.1.2 2.1.3 […]
Paper: Code Gradients: Towards Automated Traceability of LLM-Generated Code. 2024 1 What do they do, and what’s the result? 1.1 What do they do? 1.1.1 1.1.2 1.1.3 1.2 What’s the result? 1.2.1 1.2.2 1.2.3 2: Can I use any of it in my work? If yes, how? 2.1 … 2.1.1 2.1.2 2.1.3 […]
Paper: Can LLMs Generate Higher Quality Code Than Human? An Empirical Study. 2025 1 What do they do, and what’s the result? 1.1 What do they do? 1.1.1 This paper conducts an empirical analysis of AI-generated code to assess whether LLMs can produce correct and higher-quality code than humans. It evaluates the code quality of […]
Paper: Language Models in Software Development Tasks: An Emperimental Analysis of Energy and Accuracy. 2025 1 What do they do, and what’s the result? 1.1 What do they do? They explore the trade-off between model accuracy and energy consumption, so that help developers to select a language model. They investigate the performance of 18 LLMs […]
Paper: Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code. 2025 1 What do they do, and what’s the result? 1.1 What do they do? 1. Two primary methodologies are used in LLMs for code: prompt engineering and fine-tuning. In this paper, they conduct a comparative analysis between these two methodologies. 2. […]
This blog is combing several code metrics tools which introduced in the paper ‘Can LLMs Generate Higher Quality Code Than Humans? An Empirical Study‘. Some context are referenced directly from the paper. For more details, please see the paper. 1 Code Quality Metric 1.1 Code quality Metric 1.2 Human-centric metrics 1.3 Critical code quality metrics […]