Copertina

Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Xue, Zhipeng, Zhang, Xiaoting, Gao, Zhipeng, Hu, Xing, Gao, Shan, Xia, Xin, Li, Shanping
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Software Engineering
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

SelfPiCo: Self-Guided Partial Code Execution with LLMs
di: Xue, Zhipeng, et al.
Pubblicazione: (2024)

Actionable Warning Is Not Enough: Recommending Valid Actionable Warnings with Weak Supervision
di: Xue, Zhipeng, et al.
Pubblicazione: (2025)

Easy over Hard: A Simple Baseline for Test Failures Causes Prediction
di: Gao, Zhipeng, et al.
Pubblicazione: (2024)

Automating TODO-missed Methods Detection and Patching
di: Gao, Zhipeng, et al.
Pubblicazione: (2024)

Towards Better Answers: Automated Stack Overflow Post Updating
di: Mai, Yubo, et al.
Pubblicazione: (2024)

Verify Implementation Equivalence of Large Models
di: Zhan, Qi, et al.
Pubblicazione: (2026)

Less is More: On the Importance of Data Quality for Unit Test Generation
di: Zhang, Junwei, et al.
Pubblicazione: (2025)

Clean Code In Practice: Challenges and Opportunities
di: Yan, Dapeng, et al.
Pubblicazione: (2025)

Automating Comment Generation for Smart Contract from Bytecode
di: Xiang, Jianhang, et al.
Pubblicazione: (2025)

Code2API: A Tool for Generating Reusable APIs from Stack Overflow Code Snippets
di: Mai, Yubo, et al.
Pubblicazione: (2025)

Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
di: ANONYMOUS
Pubblicazione: (2025)

The Current Challenges of Software Engineering in the Era of Large Language Models
di: Gao, Cuiyun, et al.
Pubblicazione: (2024)

Diffploit: Facilitating Cross-Version Exploit Migration for Open Source Library Vulnerabilities
di: Chen, Zirui, et al.
Pubblicazione: (2025)

PS$^3$: Precise Patch Presence Test based on Semantic Symbolic Signature
di: Zhan, Qi, et al.
Pubblicazione: (2023)

Automating Zero-Shot Patch Porting for Hard Forks
di: Pan, Shengyi, et al.
Pubblicazione: (2024)

Mitigating Implicit Inconsistencies in Patch Porting
di: Pan, Shengyi, et al.
Pubblicazione: (2026)

FGIT: Fault-Guided Fine-Tuning for Code Generation
di: Fan, Lishui, et al.
Pubblicazione: (2025)

Exploring the Capabilities of LLMs for Code Change Related Tasks
di: Fan, Lishui, et al.
Pubblicazione: (2024)

Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond
di: Gao, Zeyu, et al.
Pubblicazione: (2025)

Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models
di: Gao, Shuzheng, et al.
Pubblicazione: (2024)

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

SACS: A Code Smell Dataset using Semi-automatic Generation Approach
di: Zhang, Hanyu, et al.
Pubblicazione: (2026)

CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics
di: Li, Yikun, et al.
Pubblicazione: (2024)

Specification and Detection of LLM Code Smells
di: Mahmoudi, Brahim, et al.
Pubblicazione: (2025)

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

An Empirical Study of Interaction Smells in Multi-Turn Human-LLM Collaborative Code Generation
di: Zhang, Binquan, et al.
Pubblicazione: (2026)

CupCleaner: A Hybrid Data Cleaning Approach for Comment Updating
di: Liang, Qingyuan, et al.
Pubblicazione: (2023)

ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision?
di: Fan, Lishui, et al.
Pubblicazione: (2026)

Are Human Rules Necessary? Generating Reusable APIs with CoT Reasoning and In-Context Learning
di: Mai, Yubo, et al.
Pubblicazione: (2024)

ActRef: Enhancing the Understanding of Python Code Refactoring with Action-Based Analysis
di: Wang, Siqi, et al.
Pubblicazione: (2025)

A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Code Smell Detection
di: Zhang, Beiqi, et al.
Pubblicazione: (2024)

An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities
di: Yang, Zezhou, et al.
Pubblicazione: (2025)

A Causal Perspective on Measuring, Explaining and Mitigating Smells in LLM-Generated Code
di: Velasco, Alejandro, et al.
Pubblicazione: (2025)

The Influence of Code Smells in Efferent Neighbors on Class Stability
di: Zhang, Zushuai, et al.
Pubblicazione: (2026)

An Event-Driven Tool for Context-Aware Code Smell Detection Using SmellDSL
di: Viegas, Matheus dos Santos, et al.
Pubblicazione: (2026)

Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
di: Pan, Zhiyuan, et al.
Pubblicazione: (2025)

On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software
di: Mahbub, Riasat, et al.
Pubblicazione: (2024)

Empirical Characterization of Logging Smells in Machine Learning Code
di: Foalem, Patrick Loic, et al.
Pubblicazione: (2026)

Empirical Characterization of Logging Smells in Machine Learning Code
di: Foalem, Patrick Loic, et al.
Pubblicazione: (2026)

Automated Unit Test Refactoring
di: Gao, Yi, et al.
Pubblicazione: (2024)