Výsledky vyhledávání - "code smell detection"

Exploring the role of project status information in effective code smell detection

Autoři: Alkharabsheh, Khalid Alawadi, Sadi Crespo, Yania a další

Zdroj: Cluster Computing. 28(1)

Témata: Code smell detection, Machine learning, Maintainability, Software quality, Computer software selection and evaluation, Random forests, Code smell, Feature data, Features selection, Machine learning techniques, Machine-learning, Numerical information, Status informations, Training sets

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-27051
https://doi.org/10.1007/s10586-024-04724-9

FedCSD: A Federated Learning Based Approach for Code-Smell Detection

Autoři: Alawadi, Sadi Alkharabsheh, Khalid Alkhabbas, Fahed a další

Zdroj: IEEE Access. 12:44888-44904

Témata: code smell detection, federated learning, privacy-preserving, Software quality, technical debit, Application programs, Codes (symbols), Computer software maintenance, Computer software selection and evaluation, Cryptography, Object oriented programming, Odors, Code, Code smell, Ho-momorphic encryptions, Homomorphic-encryptions, Object oriented modelling, Privacy preserving, Data privacy

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-26101
https://doi.org/10.1109/ACCESS.2024.3380167

Smell-ML: A Machine Learning Framework for Detecting Rarely Studied Code Smells

Autoři: Esraa Hamouda Abeer El-Korany Soha Makady

Zdroj: IEEE Access, Vol 13, Pp 12966-12980 (2025)

Témata: machine learning, ensemble learning, Electrical engineering. Electronics. Nuclear engineering, data balancing, Code smell detection, multi-level classification, TK1-9971

Přístupová URL adresa: https://doaj.org/article/e74ef17004cd4af98cda57b4103b9f19

Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection

Autoři: Alı Nızam Ertuğrul İslamoğlu Ömer Kerem Adali a další

Zdroj: IEEE Access, Vol 13, Pp 31335-31350 (2025)

Témata: triplet loss, contrastive learning, code smell detection, Code embedding, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Přístupová URL adresa: https://doaj.org/article/9fe6b8a447d3493090bfb60feea8039c

A study for method-level code smells detection using machine learning algorithms

Autoři: Rajwant Singh Rao Seema Dewangan Alok Mishra a další

Zdroj: Systems and Soft Computing, Vol 7, Iss , Pp 200346- (2025)

Témata: Code smell detection, Method level smells, Machine learning algorithms, Information Gain feature selection algorithm, Class balancer technique, Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95

Popis souboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2772941925001644; https://doaj.org/toc/2772-9419

Přístupová URL adresa: https://doaj.org/article/fc2f0e0ef81c44648e3b00f2d57c212a

Determining the capabilities of generative artificial intelligence tools to increase the efficiency of refactoring process

Zdroj: Technology audit and production reserves; Vol. 3 No. 2(83) (2025): Information and control systems; 6-11
Technology audit and production reserves; Том 3 № 2(83) (2025): Інформаційно-керуючі системи; 6-11

Témata: покращення якості коду, AI-driven refactoring, generative AI tools, оптимізація програмного забезпечення, automated code smell detection, автоматичне виявлення проблем у коді, software optimization, рефакторинг на основі ШІ, інструменти генеративного ШІ, code quality improvement

Popis souboru: application/pdf

Přístupová URL adresa: https://journals.uran.ua/tarp/article/view/326899

An Evaluation of Multi-Label Classification Approaches for Method-Level Code Smells Detection

Autoři: Pravin Singh Yadav Rajwant Singh Rao Alok Mishra

Zdroj: IEEE Access, Vol 12, Pp 53664-53676 (2024)
53664-53676
IEEE Access

Témata: Z-score, 0202 electrical engineering, electronic engineering, information engineering, parameter tuning, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Code smell, random search cross-validation, cross-validation, multi-label code smell detection, TK1-9971

Popis souboru: application/pdf

Přístupová URL adresa: https://doaj.org/article/f39d7c7bdd004a35abb888a8c6d53730
https://hdl.handle.net/11250/3127777

An Empirical Evaluation of Ensemble Models for Python Code Smell Detection

Autoři: Rajwant Singh Rao Seema Dewangan Alok Mishra

Zdroj: Applied Sciences, Vol 15, Iss 13, p 7472 (2025)

Témata: chi-square feature selection technique, ensemble learning models, Python code smell detection, SMOTE class balancing method, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/13/7472; https://doaj.org/toc/2076-3417

Přístupová URL adresa: https://doaj.org/article/d5355a98e88646b997415dd704137668

Enhancing Software Quality with AI: A Transformer-Based Approach for Code Smell Detection

Autoři: Israr Ali Syed Sajjad Hussain Rizvi Syed Hasan Adil

Zdroj: Applied Sciences ; Volume 15 ; Issue 8 ; Pages: 4559

Témata: code smell detection, software quality, AI-driven software engineering, Transformers, deep learning, relation-aware embeddings, automated code analysis

Geografické téma: agris

Popis souboru: application/pdf

Relation: Computing and Artificial Intelligence; https://dx.doi.org/10.3390/app15084559

Dostupnost: https://doi.org/10.3390/app15084559

Optimizing Pre-Trained Code Embeddings With Triplet Loss for Code Smell Detection

Autoři: İslamoğlu, Ertuğrul Adalı, Ömer Kerem Aydın, Musa a další

Přispěvatelé: İslamoğlu, Ertuğrul Adalı, Ömer Kerem Aydın, Musa a další

Témata: Code embedding, Contrastive learning, Triplet loss, Code smell detection

Popis souboru: application/pdf

Relation: IEEE Access; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; https://ieeexplore.ieee.org/document/10890964; https://hdl.handle.net/11352/5189; 13; 16

Dostupnost: https://hdl.handle.net/11352/5189
https://ieeexplore.ieee.org/document/10890964

Enhancing Refactoring Prediction at the Method-Level Using Stacking and Boosting Models

Autoři: Khaleel, Shahbaa I. Ahmed, Rasha

Zdroj: Jurnal Ilmiah Teknik Elektro Komputer dan Informatika; Vol. 11 No. 2 (2025): June; 276-289 ; 2338-3062 ; 2338-3070

Témata: Software Refactoring, Method-Level Refactoring, Meta-Learning, Code Smell Detection, Software Maintainability, Swarm Optimization Algorithm, Lion Optimization Algorithm, Stacking Algorithm, Boosting Algorithm

Popis souboru: application/pdf

Relation: https://journal.uad.ac.id/index.php/JITEKI/article/view/30839/13079; https://journal.uad.ac.id/index.php/JITEKI/article/view/30839

Dostupnost: https://journal.uad.ac.id/index.php/JITEKI/article/view/30839
https://doi.org/10.26555/jiteki.v11i2.30839

A Preliminary Interview Study on Developers’ Perceptions of Code Smell Detection in Industry

Autoři: Ribeiro, Felipe Fernandes, Eduardo Figueiredo, Eduardo

Zdroj: Communications in Computer and Information Science ISBN: 9783031702440

Témata: Interview study, Software tool, Code smell detection

Přístupová URL adresa: https://portal.findresearcher.sdu.dk/da/publications/e3e5b60d-0bec-46ad-a812-14799a9b2e30
https://doi.org/10.1007/978-3-031-70245-7_24

Deteksi Code Smell dengan Pendekatan Machine Learning: Analisis Bibliometrik

Autoři: Kusuma, Ellysha

Zdroj: bit-Tech; Vol. 7 No. 2 (2024): bit-Tech; 366-376 ; 2622-2728 ; 2622-271X ; 10.32877/bt.v7i2

Témata: Analisis Bibliometrik, Code Smell Detection, Machine Learning, Nvivo, Perangkat Lunak, code smell, pendeteksian

Popis souboru: application/pdf

Relation: https://jurnal.kdi.or.id/index.php/bt/article/view/1822/984; https://jurnal.kdi.or.id/index.php/bt/article/view/1822

Dostupnost: https://jurnal.kdi.or.id/index.php/bt/article/view/1822
https://doi.org/10.32877/bt.v7i2.1822

A Novel Approach for Code Smell Detection: An Empirical Study

Autoři: Seema Dewangan Rajwant Singh Rao Alok Mishra a další

Zdroj: IEEE Access, Vol 9, Pp 162869-162883 (2021)
162869-162883
IEEE Access

Témata: machine learning techniques, feature selection, code smell detection, 0202 electrical engineering, electronic engineering, information engineering, parameter optimization, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Code smell, TK1-9971

Popis souboru: application/pdf

Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09641807.pdf
https://doaj.org/article/d6b0149679174292a03622a1620db9ec
https://hdl.handle.net/11250/3098935

Machine Learning-Based Methods for Code Smell Detection: A Survey

Autoři: Pravin Singh Yadav Rajwant Singh Rao Alok Mishra a další

Zdroj: Applied Sciences, Vol 14, Iss 14, p 6149 (2024)

Témata: antipattern, code smell, code smell detection, identification, machine learning algorithms, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/14/6149; https://doaj.org/toc/2076-3417

Přístupová URL adresa: https://doaj.org/article/0cbf56c35776470da589e2a26f280f6d

Reducing redundancy of test cases generation using code smell detection and refactoring

Autoři: Rosziati Ibrahim Maryam Ahmed Richi Nayak a další

Zdroj: Journal of King Saud University: Computer and Information Sciences, Vol 32, Iss 3, Pp 367-374 (2020)

Témata: Duplicate code smell, Refactoring, Electronic computers. Computer science, 0202 electrical engineering, electronic engineering, information engineering, TJ Mechanical engineering and machinery, Small method, QA75.5-76.95, 02 engineering and technology, Lazy class, Code smell detection

Popis souboru: application/pdf; text

Přístupová URL adresa: https://doaj.org/article/5affe6cc3c6f4dccb10d78936f433d4a
https://www.sciencedirect.com/science/article/pii/S1319157818300296
https://dblp.uni-trier.de/db/journals/jksucis/jksucis32.html#IbrahimANJ20
https://qa-eprints.qut.edu.au/198264/
https://eprints.qut.edu.au/198264/

Replication package for the paper: 'Attentionsmelling: Using Large Language Models to Identify Code Smells'

Autoři: Gomes, Andre orcid:0009-0001-3910- Sousa, Denis a další

Přispěvatelé: Gomes, Andre orcid:0009-0001-3910- Sousa, Denis a další

Témata: Large Language Model, Code Smell Detection, Software Quality, Prompt Engineering, Software Maintenance, Neural Networks

Relation: https://zenodo.org/records/15991616; oai:zenodo.org:15991616; https://doi.org/10.5281/zenodo.15991616

Dostupnost: https://doi.org/10.5281/zenodo.15991616
https://zenodo.org/records/15991616

SmellyCode++: Multi-Label Dataset for Code Smell Detection

Autoři: Nawaf Alomari Amal Alazba Hamoud Aljamaan a další

Témata: Software Engineering, code smells, design patterns, empirical study, Code smell detection, Design smells, code refactoring, code refactorization, Deep Learning Approaches

Dostupnost: https://doi.org/10.6084/m9.figshare.28531922.v1
https://figshare.com/articles/dataset/SmellyCode_Multi-Label_Dataset_for_Code_Smell_Detection/28531922

Selected documents on Grey Literature with Findings - SBES 2025

Autoři: Anonimous

Témata: Empirical software engineering, Code Smell Detection, Systematic Literature Review, Machine learning

Dostupnost: https://doi.org/10.6084/m9.figshare.29213057.v1
https://figshare.com/articles/dataset/Selected_documents_on_Grey_Literature_with_Findings_-_SBES_2025/29213057

Code Smell Detection Using Ensemble Machine Learning Algorithms

Autoři: Seema Dewangan Rajwant Singh Rao Alok Mishra a další

Zdroj: Applied Sciences, Vol 12, Iss 10321, p 10321 (2022)

Témata: code smell, code smell detection, ensemble method, deep learning, Chi-square feature extraction technique, SMOTE class balancing technique, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/12/20/10321; https://doaj.org/toc/2076-3417; https://doaj.org/article/ceadbaab626148909d67c379681a3ee8

Dostupnost: https://doi.org/10.3390/app122010321
https://doaj.org/article/ceadbaab626148909d67c379681a3ee8