Copertina

On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Mahbub, Riasat, Rahman, Mohammad Masudur, Habib, Muhammad Ahsanul
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Software Engineering
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Explaining Software Bugs Leveraging Code Structures in Neural Machine Translation
di: Mahbub, Parvez, et al.
Pubblicazione: (2022)

Towards Understanding the Impact of Data Bugs on Deep Learning Models in Software Engineering
di: Shah, Mehil B, et al.
Pubblicazione: (2024)

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

Understanding the Impact of Domain Term Explanation on Duplicate Bug Report Detection
di: Mukherjee, Usmi, et al.
Pubblicazione: (2025)

Be a Partner, not a Bystander in Software Engineering Practice: Bridging the Gaps between Academia and Industry
di: Rahman, Mohammad Masudur, et al.
Pubblicazione: (2026)

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification
di: Jahan, Sigma, et al.
Pubblicazione: (2025)

Can We Identify Stack Overflow Questions Requiring Code Snippets? Investigating the Cause & Effect of Missing Code Snippets
di: Mondal, Saikat, et al.
Pubblicazione: (2024)

Can Hessian-Based Insights Support Fault Diagnosis in Attention-based Models?
di: Jahan, Sigma, et al.
Pubblicazione: (2025)

Do Internal Software Metrics Have Relationship with Fault-proneness and Change-proneness?
di: Rahman, Md. Masudur, et al.
Pubblicazione: (2023)

Improving IR-based Bug Localization with Semantics-Driven Query Reduction
di: Samir, Asif Mohammed, et al.
Pubblicazione: (2025)

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

Towards Understanding the Challenges of Bug Localization in Deep Learning Systems
di: Jahan, Sigma, et al.
Pubblicazione: (2024)

Characterizing Faults in Agentic AI: A Taxonomy of Types, Symptoms, and Root Causes
di: Shah, Mehil B, et al.
Pubblicazione: (2026)

Towards a Taxonomy of Software Log Smells
di: Saarimäki, Nyyti, et al.
Pubblicazione: (2024)

A Validated Taxonomy on Software Energy Smells
di: Mehditabar, Mohammadjavad, et al.
Pubblicazione: (2026)

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

How Do Communities of ML-Enabled Systems Smell? A Cross-Sectional Study on the Prevalence of Community Smells
di: Annunziata, Giusy, 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)

Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
di: Shah, Mehil B., et al.
Pubblicazione: (2024)

How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study
di: Velasco, Alejandro, et al.
Pubblicazione: (2024)

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

The Code Whisperer: LLM and Graph-Based AI for Smell and Vulnerability Resolution
di: Baqar, Mohammad, et al.
Pubblicazione: (2026)

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

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

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

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

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

Clean Code, Better Models: Enhancing LLM Performance with Smell-Cleaned Dataset
di: Xue, Zhipeng, et al.
Pubblicazione: (2025)

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

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

Empirical Study of the Docker Smells Impact on the Image Size
di: Durieux, Thomas
Pubblicazione: (2023)

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

From Code Smells to Best Practices: Tackling Resource Leaks in PyTorch, TensorFlow, and Keras
di: Abdallah, Bashar, et al.
Pubblicazione: (2025)

Why Attention Fails: A Taxonomy of Faults in Attention-Based Neural Networks
di: Jahan, Sigma, et al.
Pubblicazione: (2025)

Measuring LLM Trust Allocation Across Conflicting Software Artifacts
di: Ulfat, Noshin, et al.
Pubblicazione: (2026)

Socio-Technical Well-Being of Quantum Software Communities: An Overview on Community Smells
di: Lambiase, Stefano, et al.
Pubblicazione: (2026)

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)

Approaching Code Search for Python as a Translation Retrieval Problem with Dual Encoders
di: Khan, Monoshiz Mahbub, et al.
Pubblicazione: (2024)

Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent
di: Shah, Mehil B, et al.
Pubblicazione: (2025)