Imagen de Portada

Training LLMs for Generating IEC 61131-3 Structured Text with Online Feedback

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Haag, Aaron, Fuchs, Bertram, Kacan, Altay, Lohse, Oliver
Formato:	Preprint
Publicado:	2024
Materias:	Software Engineering
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

FeatureFox: Sample-Efficient Panoptic Graph Segmentation for Machining Feature Recognition in B-Rep 3D-CAD Models
por: Fuchs, Bertram, et al.
Publicado: (2026)

Joint Embeddings for Graph Instruction Tuning
por: Haag, Aaron, et al.
Publicado: (2024)

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs
por: Groen, Eduard C., et al.
Publicado: (2025)

From Image to UML: First Results of Image Based UML Diagram Generation Using LLMs
por: Conrardy, Aaron, et al.
Publicado: (2024)

May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
por: Yang, Shaoyu, et al.
Publicado: (2025)

From Text to DSL: Evaluating Grammar-Based Model Generation Using Open LLMs
por: Baber, Junaid, et al.
Publicado: (2026)

RITA: A Tool for Automated Requirements Classification and Specification from Online User Feedback
por: Mallya, Manjeshwar Aniruddh, et al.
Publicado: (2026)

Logging Like Humans for LLMs: Rethinking Logging via Execution and Runtime Feedback
por: Wang, Xin, et al.
Publicado: (2026)

From Online User Feedback to Requirements: Evaluating Large Language Models for Classification and Specification Tasks
por: Mallya, Manjeshwar Aniruddh, et al.
Publicado: (2025)

Helping LLMs Improve Code Generation Using Feedback from Testing and Static Analysis
por: Dolcetti, Greta, et al.
Publicado: (2024)

Inside Out: Uncovering How Comment Internalization Steers LLMs for Better or Worse
por: Imani, Aaron, et al.
Publicado: (2025)

NESSiE: The Necessary Safety Benchmark -- Identifying Errors that should not Exist
por: Bertram, Johannes, et al.
Publicado: (2026)

Patch-CLIP: A Patch-Text Pre-Trained Model
por: Tang, Xunzhu, et al.
Publicado: (2023)

Generating Structured Plan Representation of Procedures with LLMs
por: Garg, Deepeka, et al.
Publicado: (2025)

Q-COSMIC: Quantum Software Metrics Based on COSMIC (ISO/IEC19761)
por: Valdes-Souto, Francisco, et al.
Publicado: (2024)

CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation
por: Cai, Jianfeng, et al.
Publicado: (2026)

TestForge: Feedback-Driven, Agentic Test Suite Generation
por: Jain, Kush, et al.
Publicado: (2025)

Can User Feedback Help Issue Detection? An Empirical Study on a One-billion-user Online Service System
por: Jiang, Shuyao, et al.
Publicado: (2025)

Boosting LLMs for Mutation Generation
por: Wang, Bo, et al.
Publicado: (2026)

Intent Preserving Generation of Diverse and Idiomatic (Code-)Artifacts
por: Westphal, Oliver
Publicado: (2025)

BugMentor: Generating Answers to Follow-up Questions from Software Bug Reports using Structured Information Retrieval and Neural Text Generation
por: Mukherjee, Usmi, et al.
Publicado: (2023)

An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face
por: Liu, Yujian, et al.
Publicado: (2026)

Heterogeneous Prompting and Execution Feedback for SWE Issue Test Generation and Selection
por: Ahmed, Toufique, et al.
Publicado: (2025)

Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
por: Moon, Seungjun, et al.
Publicado: (2023)

Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates
por: Sun, Zhihong, et al.
Publicado: (2024)

Echo: Graph-Enhanced Retrieval and Execution Feedback for Issue Reproduction Test Generation
por: Fei, Zhiwei, et al.
Publicado: (2026)

Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
por: He, Kaifeng, et al.
Publicado: (2026)

On Evaluating the Efficiency of Source Code Generated by LLMs
por: Niu, Changan, et al.
Publicado: (2024)

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
por: Blyth, Scott, et al.
Publicado: (2025)

Enhancing Large Language Models for Text-to-Testcase Generation
por: Alagarsamy, Saranya, et al.
Publicado: (2024)

TOGLL: Correct and Strong Test Oracle Generation with LLMs
por: Hossain, Soneya Binta, et al.
Publicado: (2024)

Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs
por: Zhu, Yuqi, et al.
Publicado: (2025)

TestWeaver: Execution-aware, Feedback-driven Regression Testing Generation with Large Language Models
por: Le, Cuong Chi, et al.
Publicado: (2025)

Context Conquers Parameters: Outperforming Proprietary LLM in Commit Message Generation
por: Imani, Aaron, et al.
Publicado: (2024)

PyTester: Deep Reinforcement Learning for Text-to-Testcase Generation
por: Takerngsaksiri, Wannita, et al.
Publicado: (2024)

From Specifications to Prompts: On the Future of Generative LLMs in Requirements Engineering
por: Vogelsang, Andreas
Publicado: (2024)

ASTER: Natural and Multi-language Unit Test Generation with LLMs
por: Pan, Rangeet, et al.
Publicado: (2024)

AutoPLC: Generating Vendor-Aware Structured Text for Programmable Logic Controllers
por: Yang, Donghao, et al.
Publicado: (2024)

Integrating Symbolic Execution with LLMs for Automated Generation of Program Specifications
por: Yang, Fanpeng, et al.
Publicado: (2025)

Explicit Vulnerability Generation with LLMs: An Investigation Beyond Adversarial Attacks
por: Bosnak, Emir, et al.
Publicado: (2025)