:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Mu, Wenchuan, Lim, Kwan Hui
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Software Engineering Artificial Intelligence
Accesso online:	https://arxiv.org/abs/2404.16457
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Towards Reliable Evaluation of Neural Program Repair with Natural Robustness Testing
di: Le-Cong, Thanh, et al.
Pubblicazione: (2024)

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

Label-Free Topic-Focused Summarization Using Query Augmentation
di: Mu, Wenchuan, et al.
Pubblicazione: (2024)

OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering
di: Imran, Mia Mohammad, et al.
Pubblicazione: (2025)

Benchmarking Harmonized Tariff Schedule Classification Models
di: Judy, Bryce
Pubblicazione: (2024)

Model Provenance via Model DNA
di: Mu, Xin, et al.
Pubblicazione: (2023)

Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
di: Zhang, Lingzhe, et al.
Pubblicazione: (2026)

Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning
di: Long, Guoming, et al.
Pubblicazione: (2026)

Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
di: González, Alexandra, et al.
Pubblicazione: (2024)

Beyond Retrieval: A Multitask Benchmark and Model for Code Search
di: Xue, Siqiao, et al.
Pubblicazione: (2026)

Conventional Commit Classification using Large Language Models and Prompt Engineering
di: Quadir, H. M. Sazzad, et al.
Pubblicazione: (2026)

CodeFort: Robust Training for Code Generation Models
di: Zhang, Yuhao, et al.
Pubblicazione: (2024)

Exploring the Potential of Large Language Models in Fine-Grained Review Comment Classification
di: Nguyen, Linh, et al.
Pubblicazione: (2025)

DRAGON: Robust Classification for Very Large Collections of Software Repositories
di: Balla, Stefano, et al.
Pubblicazione: (2026)

Towards a General Framework for HTN Modeling with LLMs
di: Puerta-Merino, Israel, et al.
Pubblicazione: (2025)

Towards Leveraging Large Language Model Summaries for Topic Modeling in Source Code
di: Carissimi, Michele, et al.
Pubblicazione: (2025)

Unveiling Project-Specific Bias in Neural Code Models
di: Li, Zhiming, et al.
Pubblicazione: (2022)

Precision in Practice: Knowledge Guided Code Summarizing Grounded in Industrial Expectations
di: Li, Jintai, et al.
Pubblicazione: (2026)

Towards a Neural Debugger for Python
di: Beck, Maximilian, et al.
Pubblicazione: (2026)

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization
di: Wang, Huanting, et al.
Pubblicazione: (2024)

Post-Incorporating Code Structural Knowledge into Pretrained Models via ICL for Code Translation
di: Du, Yali, et al.
Pubblicazione: (2025)

Towards a Domain-Specific Modelling Environment for Reinforcement Learning
di: Sinani, Natalie, et al.
Pubblicazione: (2024)

CodeSSM: Towards State Space Models for Code Understanding
di: Verma, Shweta, et al.
Pubblicazione: (2025)

Are Large Language Models Robust in Understanding Code Against Semantics-Preserving Mutations?
di: Orvalho, Pedro, et al.
Pubblicazione: (2025)

Towards Better Code Understanding in Decoder-Only Models with Contrastive Learning
di: Lin, Jiayi, et al.
Pubblicazione: (2024)

Towards a Digital Twin Modeling Method for Container Terminal Port
di: Hakimi, Faouzi, et al.
Pubblicazione: (2025)

An Experience Report on Regression-Free Repair of Deep Neural Network Model
di: Nakagawa, Takao, et al.
Pubblicazione: (2025)

Robustness and Reasoning Fidelity of Large Language Models in Long-Context Code Question Answering
di: Maharaj, Kishan, et al.
Pubblicazione: (2026)

A-ProS: Towards Reliable Autonomous Programming Through Multi-Model Feedback
di: Tabassum, Anika, et al.
Pubblicazione: (2026)

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

WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models
di: Lei, Xinping, et al.
Pubblicazione: (2026)

Towards Advancing Code Generation with Large Language Models: A Research Roadmap
di: Jin, Haolin, et al.
Pubblicazione: (2025)

Toward a Theory of Causation for Interpreting Neural Code Models
di: Palacio, David N., et al.
Pubblicazione: (2023)

PACIFIC: a framework for generating benchmarks to check Precise Automatically Checked Instruction Following In Code
di: Dreyfuss, Itay, et al.
Pubblicazione: (2025)

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization
di: Lange, Robert Tjarko, et al.
Pubblicazione: (2025)

Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy
di: Taherkhani, Hamed, et al.
Pubblicazione: (2024)

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models
di: Xu, Jingxuan, et al.
Pubblicazione: (2025)

RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
di: Lin, Feng, et al.
Pubblicazione: (2025)

When Prompts Go Wrong: Evaluating Code Model Robustness to Ambiguous, Contradictory, and Incomplete Task Descriptions
di: Larbi, Maya, et al.
Pubblicazione: (2025)

The Complexity Trap: Simple Observation Masking Is as Efficient as LLM Summarization for Agent Context Management
di: Lindenbauer, Tobias, et al.
Pubblicazione: (2025)