arXiv AI
LLM Code Smells: A Taxonomy and Detection Approach
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 1
SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs
arXiv:2509. 20491v3 Announce Type: replace-cross Abstract: Machine Learning (ML) pipelines encode quality-relevant decisions across data preparation, training, evaluation, and configuration code.
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
Jul 14
Mitigating LLM Sycophancy in Code Smell Detection Using Evidence-Guided Reasoning Prompts
arXiv:2607. 10411v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code smell detection tasks due to their ability to interpret program semantics.
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
Sep 25
Detecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning
The paper introduces CodeScan, a black-box, vulnerability-oriented scanning framework designed to detect data poisoning and backdoor attacks in code generation large language models (LLMs). CodeScan operates by analyzing structural similarities across multiple code generations, normalizing them with abstract syntax tree (AST) techniques, and then applying LLM-based vulnerability analysis to identify recurring insecure patterns. Evaluations on 117 models across three architectures and multiple sizes show over 97% detection accuracy with fewer false positives compared to prior methods.
arXiv AI
Oct 21
Hallucinations of Large Language Models in Cross-Language Code Translation: Phenomena and Mitigation
arXiv:2610.27868v1 Announce Type: cross Abstract: Code translation converts source code from one programming language to another while preserving its functionality. Although Large Language Models (LL...