Cover Image

Toward Automated and Trustworthy Scientific Analysis and Visualization with LLM-Generated Code

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chakroborti, Apu Kumar, Ding, Yi, Wan, Lipeng
Format:	Preprint
Published:	2025
Subjects:	Software Engineering Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation
by: Chen, Qiaosheng, et al.
Published: (2025)

TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework
by: Gao, Shuzheng, et al.
Published: (2025)

CodeCircuit: Toward Inferring LLM-Generated Code Correctness via Attribution Graphs
by: He, Yicheng, et al.
Published: (2026)

Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation
by: Cui, Yi
Published: (2025)

Deploy-Master: Automating the Deployment of 50,000+ Agent-Ready Scientific Tools in One Day
by: Wang, Yi, et al.
Published: (2026)

Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards
by: Zhang, Zejun, et al.
Published: (2026)

Defective Task Descriptions in LLM-Based Code Generation: Detection and Analysis
by: Akli, Amal, et al.
Published: (2026)

From Helpful to Trustworthy: LLM Agents for Pair Programming
by: Ayon, Ragib Shahariar
Published: (2026)

Contract-Coding: Towards Repo-Level Generation via Structured Symbolic Paradigm
by: Lin, Yi, et al.
Published: (2026)

AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing
by: Nunez, Ana, et al.
Published: (2024)

AgenticTCAD: A LLM-based Multi-Agent Framework for Automated TCAD Code Generation and Device Optimization
by: Fan, Guangxi, et al.
Published: (2025)

AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
by: Zhu, Yuecai, et al.
Published: (2026)

Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis
by: Khati, Dipin, et al.
Published: (2026)

Automated Benchmark Generation for Repository-Level Coding Tasks
by: Vergopoulos, Konstantinos, et al.
Published: (2025)

BugSpotter: Automated Generation of Code Debugging Exercises
by: Pădurean, Victor-Alexandru, et al.
Published: (2024)

Investigating The Smells of LLM Generated Code
by: Paul, Debalina Ghosh, et al.
Published: (2025)

QLPro: Automated Code Vulnerability Discovery via LLM and Static Code Analysis Integration
by: Hu, Junze, et al.
Published: (2025)

From Articles to Code: On-Demand Generation of Core Algorithms from Scientific Publications
by: Movassaghi, Cameron S., et al.
Published: (2025)

Augmenting Large Language Models with Static Code Analysis for Automated Code Quality Improvements
by: Abtahi, Seyed Moein, et al.
Published: (2025)

Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
by: Palacio, David N., et al.
Published: (2024)

AutoAssert 1: A LoRA Fine-Tuned LLM Model for Efficient Automated Assertion Generation
by: Zhong, Yi, et al.
Published: (2025)

scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns
by: Samsonau, Sergey V.
Published: (2026)

LLM Code Customization with Visual Results: A Benchmark on TikZ
by: Reux, Charly, et al.
Published: (2025)

Automated Proof Generation for Rust Code via Self-Evolution
by: Chen, Tianyu, et al.
Published: (2024)

Code Copycat Conundrum: Demystifying Repetition in LLM-based Code Generation
by: Liu, Mingwei, et al.
Published: (2025)

LogReasoner: Empowering LLMs with Expert-like Coarse-to-Fine Reasoning for Automated Log Analysis
by: Ma, Lipeng, et al.
Published: (2025)

Uncertainty Quantification for LLM-based Code Generation
by: Xu, Senrong, et al.
Published: (2026)

Towards Better Correctness and Efficiency in Code Generation
by: Feng, Yunlong, et al.
Published: (2025)

QSpark: Towards Reliable Qiskit Code Generation
by: Kheiri, Kiana, et al.
Published: (2025)

A Comparative Analysis of Large Language Models for Code Documentation Generation
by: Dvivedi, Shubhang Shekhar, et al.
Published: (2023)

Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation
by: Vartziotis, Tina, et al.
Published: (2024)

LLM-Empowered Event-Chain Driven Code Generation for ADAS in SDV systems
by: Petrovic, Nenad, et al.
Published: (2025)

Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation
by: Jiang, Yongqing, et al.
Published: (2026)

Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
by: Lin, Hong Yi, et al.
Published: (2026)

A Performance Study of LLM-Generated Code on Leetcode
by: Coignion, Tristan, et al.
Published: (2024)

Bias Testing and Mitigation in LLM-based Code Generation
by: Huang, Dong, et al.
Published: (2023)

Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting
by: Ye, Tong, et al.
Published: (2024)

Previously on... Automating Code Review
by: Heumüller, Robert, et al.
Published: (2025)

Designing Empirical Studies on LLM-Based Code Generation: Towards a Reference Framework
by: Nascimento, Nathalia, et al.
Published: (2025)

CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection
by: Zhang, Qingyu, et al.
Published: (2025)