Copertina

Not All Metrics Are Necessary: Multi-label Code Smell Detection with Prompt Learning

Fuente: Zenodo

Salvato in:

Dettagli Bibliografici
Autore principale:	Hy, L
Natura:	Recurso digital
Pubblicazione:	Zenodo 2025
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Prompt Learning for Multi-Label Code Smell Detection: A Promising Approach
di: Liu, Haiyang, et al.
Pubblicazione: (2024)

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

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

ML Code Smells: From Specification to Detection
di: Mahmoudi, Brahim, et al.
Pubblicazione: (2025)

Towards Automated Detection of Inline Code Comment Smells
di: Oztas, Ipek, et al.
Pubblicazione: (2025)

An Empirical Evaluation of Code Smell Detection in Angular Applications
di: Nunes, Maykon, et al.
Pubblicazione: (2026)

Replication Package: Co-occurrence of Code Smells and Community Smells in Java Code Samples
di: Bueno, Arthur Ramires Rodrigues Neto, et al.
Pubblicazione: (2026)

FedCSD: A Federated Learning Based Approach for Code-Smell Detection
di: Alawadi, Sadi, et al.
Pubblicazione: (2023)

SmellBench: Evaluating LLM Agents on Architectural Code Smell Repair
di: Dinu, Ion George, et al.
Pubblicazione: (2026)

Automatic Identification of Machine Learning-Specific Code Smells
di: Hamfelt, Peter, et al.
Pubblicazione: (2025)

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)

Software Metric Based Impact Analysis of Code Smells ‐ A Large Scale Empirical Study
di: Md. Masudur Rahman, et al.
Pubblicazione: (2025)

When Code Smells Meet ML: On the Lifecycle of ML-specific Code Smells in ML-enabled Systems
di: Recupito, Gilberto, et al.
Pubblicazione: (2024)

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

AMUSED: Affective Metrics & Usability Smell Evaluation Dataset
di: de Souza Santos, Flávia
Pubblicazione: (2025)

Code Smell Detection via Pearson Correlation and ML Hyperparameter Optimization
di: Bhuiyan, Moinuddin Muhammad Imtiaz, et al.
Pubblicazione: (2025)

SmellyBot: An AI‐Powered Software Bot for Code Smell Detection
di: Amal Alazba, et al.
Pubblicazione: (2025)

Data Preparation for Deep Learning based Code Smell Detection: A Systematic Literature Review
di: Zhang, Fengji, et al.
Pubblicazione: (2024)

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

A Systematic Review on Code Smell Detection Approaches in Open Source Projects
di: Sawsan Alodibat, et al.
Pubblicazione: (2026)

On the Impact of Requirements Smells in Prompts: The Case of Automated Traceability
di: Vogelsang, Andreas, et al.
Pubblicazione: (2025)

Prompt Smells: An Omen for Undesirable Generative AI Outputs
di: Ronanki, Krishna, et al.
Pubblicazione: (2024)

Characterizing JavaScript Security Code Smells
di: Kambhampati, Vikas, et al.
Pubblicazione: (2024)

Automated Detection of Inter-Language Design Smells in Multi-Language Deep Learning Frameworks
di: Li, Zengyang, et al.
Pubblicazione: (2024)

Necessary but not Sufficient: Limitations of Projection Quality Metrics
di: A. Machado, et al.
Pubblicazione: (2025)

Label-Guided Prompt for Multi-label Few-shot Aspect Category Detection
di: Guan, ChaoFeng, et al.
Pubblicazione: (2024)

Is Prompt Selection Necessary for Task-Free Online Continual Learning?
di: Park, Seoyoung, et al.
Pubblicazione: (2026)

Beyond Strict Rules: Assessing the Effectiveness of Large Language Models for Code Smell Detection
di: Souza, Saymon, et al.
Pubblicazione: (2026)

A Code Smell Refactoring Approach using GNNs
di: Zhang, HanYu, et al.
Pubblicazione: (2025)

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

Copy-and-Paste? Identifying EVM-Inequivalent Code Smells in Multi-chain Reuse Contracts
di: Wang, Zexu, 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)

Not All Layers of LLMs Are Necessary During Inference
di: Fan, Siqi, et al.
Pubblicazione: (2024)

Smells-sus: Sustainability Smells in IaC
di: Kosbar, Seif, et al.
Pubblicazione: (2025)

Replication package for A Unified Cross-Language Intermediate Representation for Infrastructure-as-Code Smell Detection
di: Anonymous
Pubblicazione: (2026)

Replication package for A Unified Cross-Language Intermediate Representation for Infrastructure-as-Code Smell Detection
di: Anonymous
Pubblicazione: (2026)

CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells
di: Naik, Atharva, et al.
Pubblicazione: (2024)

A Novel Taxonomy and Classification Scheme for Code Smell Interactions
di: Gupta, Ruchin, et al.
Pubblicazione: (2025)

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