ml-Codesmell: A code smell prediction dataset for machine learning approaches-Reference-Cited by-同舟云学术

ml-Codesmell: A code smell prediction dataset for machine learning approaches

Published:2022-12 Issue: Volume: Page:
ISSN:
Container-title:The 11th International Symposium on Information and Communication Technology
language:
Short-container-title:

Author:

Nguyen Thanh Binh¹^ORCID,Nguyen N. H. Minh²^ORCID,Le Thi My Hanh³^ORCID,Nguyen Thanh Binh²^ORCID

Affiliation:

1. College of Electromechanics, Construction and Agro-forestry of Central Vietnam, Viet Nam

2. The University of Danang, Vietnam-Korea University of Information and Communication Technology, Viet Nam

3. The University of Danang, University Of Science and Technology, Viet Nam

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3568562.3568643

Reference46 articles.

1. Paris Avgeriou and P Kruchten. 2016. Managing Technical Debt in Software Engineering. Dagstuhl Seminar 16162(2016). https://doi.org/10.4230/DagRep.6.4.110 10.4230/DagRep.6.4.110

2. Paris Avgeriou and P Kruchten. 2016. Managing Technical Debt in Software Engineering. Dagstuhl Seminar 16162(2016). https://doi.org/10.4230/DagRep.6.4.110

3. Leo Breiman. 2001. Random Forests. 5-32 pages. https://doi.org/10.1023/A:1010933404324 10.1023/A:1010933404324

4. Leo Breiman. 2001. Random Forests. 5-32 pages. https://doi.org/10.1023/A:1010933404324

5. Automatic detection of design problems in object-oriented reengineering

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An Approach Based on Machine Learning for Predicting Software Design Problems;Proceedings of the XIX Brazilian Symposium on Information Systems;2023-05-29