Copertina

A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, QiHong, Yu, Jiachen, Li, Jiawei, Deng, Jiecheng, Chen, Justin Tian Jin, Ahmed, Iftekhar
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Software Engineering Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

From Bias To Improved Prompts: A Case Study of Bias Mitigation of Clone Detection Models
di: Chen, QiHong, et al.
Pubblicazione: (2025)

Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks
di: Li, Yuangang, et al.
Pubblicazione: (2026)

Is Multi-Agent Debate (MAD) the Silver Bullet? An Empirical Analysis of MAD in Code Summarization and Translation
di: Chun, Jina, et al.
Pubblicazione: (2025)

An Empirical Study on Automatically Detecting AI-Generated Source Code: How Far Are We?
di: Suh, Hyunjae, et al.
Pubblicazione: (2024)

Consider What Humans Consider: Optimizing Commit Message Leveraging Contexts Considered By Human
di: Li, Jiawei, et al.
Pubblicazione: (2025)

Does the Order of Fine-tuning Matter and Why?
di: Chen, Qihong, et al.
Pubblicazione: (2024)

Optimization is Better than Generation: Optimizing Commit Message Leveraging Human-written Commit Message
di: Li, Jiawei, et al.
Pubblicazione: (2025)

Human or LLM? A Comparative Study on Accessible Code Generation Capability
di: Suh, Hyunjae, et al.
Pubblicazione: (2025)

Aligning Requirement for Large Language Model's Code Generation
di: Tian, Zhao, et al.
Pubblicazione: (2025)

A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat
di: Yang, Zezhou, et al.
Pubblicazione: (2025)

Investigating the Impact of Code Comment Inconsistency on Bug Introducing
di: Radmanesh, Shiva, et al.
Pubblicazione: (2024)

Fixing Large Language Models' Specification Misunderstanding for Better Code Generation
di: Tian, Zhao, et al.
Pubblicazione: (2023)

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

What Makes a Great Software Quality Assurance Engineer?
di: Farias, Roselane Silva, et al.
Pubblicazione: (2024)

A Deep Dive into Large Language Models for Automated Bug Localization and Repair
di: Hossain, Soneya Binta, et al.
Pubblicazione: (2024)

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

Greening Large Language Models of Code
di: Shi, Jieke, et al.
Pubblicazione: (2023)

Beyond Self-learned Attention: Mitigating Attention Bias in Transformer-based Models Using Attention Guidance
di: Gesi, Jiri, et al.
Pubblicazione: (2024)

Where Is Self-admitted Code Generated by Large Language Models on GitHub?
di: Yu, Xiao, et al.
Pubblicazione: (2024)

PSD2Code: Automated Front-End Code Generation from Design Files via Multimodal Large Language Models
di: Chen, Yongxi, et al.
Pubblicazione: (2025)

A Survey on Evaluating Large Language Models in Code Generation Tasks
di: Chen, Liguo, et al.
Pubblicazione: (2024)

Self-planning Code Generation with Large Language Models
di: Jiang, Xue, et al.
Pubblicazione: (2023)

A Hybrid Approach for EMF Code Generation:Code Templates Meet Large Language Models
di: He, Xiao, et al.
Pubblicazione: (2025)

What Builds Effective In-Context Examples for Code Generation?
di: Li, Dongze, et al.
Pubblicazione: (2025)

What's Wrong with Your Code Generated by Large Language Models? An Extensive Study
di: Dou, Shihan, et al.
Pubblicazione: (2024)

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

Test Smell: A Parasitic Energy Consumer in Software Testing
di: Misu, Md Rakib Hossain, et al.
Pubblicazione: (2023)

DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models
di: Chen, Yunnong, et al.
Pubblicazione: (2025)

Knowledge-Aware Code Generation with Large Language Models
di: Huang, Tao, et al.
Pubblicazione: (2024)

On the Effectiveness of Large Language Models in Domain-Specific Code Generation
di: Gu, Xiaodong, et al.
Pubblicazione: (2023)

Instructive Code Retriever: Learn from Large Language Model's Feedback for Code Intelligence Tasks
di: Lu, Jiawei, et al.
Pubblicazione: (2024)

Does Documentation Matter? An Empirical Study of Practitioners' Perspective on Open-Source Software Adoption
di: Imani, Aaron, et al.
Pubblicazione: (2024)

Assertion Messages with Large Language Models (LLMs) for Code
di: Aljohani, Ahmed, et al.
Pubblicazione: (2025)

Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models
di: Yang, Guang, et al.
Pubblicazione: (2023)

When LLMs Lag Behind: Knowledge Conflicts from Evolving APIs in Code Generation
di: Ashik, Ahmed Nusayer, et al.
Pubblicazione: (2026)

Towards Understanding the Characteristics of Code Generation Errors Made by Large Language Models
di: Wang, Zhijie, et al.
Pubblicazione: (2024)

Why Personalizing Deep Learning-Based Code Completion Tools Matters
di: Giagnorio, Alessandro, et al.
Pubblicazione: (2025)

ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code
di: Feng, Jia, et al.
Pubblicazione: (2024)

Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward
di: Sergeyuk, Agnia, et al.
Pubblicazione: (2024)

Fixing Your Own Smells: Adding a Mistake-Based Familiarisation Step When Teaching Code Refactoring
di: Tan, Ivan, et al.
Pubblicazione: (2024)