arXiv AI
Using LLMs in Software Design: An Empirical Study of GitHub and A Practitioner Survey
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arXiv AI
Sep 4
Can LLMs Extract Architectural Design Decisions from Source Code Commits? - A Preliminary Exploratory Study
The study investigates whether large language models can extract Architectural Design Decisions (ADDs) from source code commits. Using four LLMs (Gemini 3 Pro, DeepSeek R1, Kimi K2, Qwen3) with zero‑shot and few‑shot prompting on 30 developer‑written ADDs, the authors evaluate outputs with ROUGE‑L, BLEU, METEOR, and BERTScore. Results show all models achieve a BERT‑F1 above 0.81, with few‑shot prompting slightly improving alignment, but the generated ADDs tend to be overly long, implementation‑focused, and lack the rationale behind the decisions.
arXiv AI
Sep 10
Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks
The study investigates how users interact with ChatGPT for code generation beyond simple function-level tasks, focusing on project-level benchmarks that involve multi-class dependencies. A user study with 36 participants examined prompting patterns, screen recordings, and chat logs to identify Human‑LLM Interaction (HLI) features that influence productivity. The results highlight three consistently supportive HLI features, five guidelines to boost productivity, and a taxonomy of 29 runtime and logic errors with mitigation strategies.
arXiv Computation and Language
Sep 22
When Who You Are Can Change the Code You Get: A Study of Persona-Induced Bias in LLM Code Generation
arXiv:2609.22102v1 Announce Type: cross Abstract: Large Language Models (LLMs) are widely used as programming assistants, yet it remains unclear whether and how user's demographic information impacts...
Hugging Face Trending Papers
Jul 2
An Exploratory Study on LLM-Generated Code and Comments in Code Repositories
The use of LLMs in software development has become increasingly widespread on tasks such as code generation and summarization. Reports from large technology companies showed that around 20% to 30% of their code are generated by LLMs.
arXiv AI
Jul 3
An Exploratory Study on LLM-Generated Code and Comments in Code Repositories
arXiv:2607. 01867v1 Announce Type: cross Abstract: The use of LLMs in software development has become increasingly widespread on tasks such as code generation and summarization.
arXiv AI
Aug 13
Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation
arXiv:2608. 11513v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software engineering workflows, helping developers write, debug, test, and maintain code.