Déjà Vu: A Replication Study on Code Smells and Faults in JavaScript Projects
Open Access
- 12 April 2026
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 504-514
Abstract
No abstract availableFunding Information
- European HORIZON-KDT-JU-2023-2-RIA (101140216-2, KDT232RIA_00017)
This publication has 33 references indexed in Scilit:
- Machine learning techniques for code smell detection: A systematic literature review and meta-analysisInformation and Software Technology, 2019
- A large-scale empirical study of code smells in JavaScript projectsSoftware Quality Journal, 2019
- JavaScript Frameworks for Modern Web Development2019
- How developers perceive smells in source code: A replicated studyInformation and Software Technology, 2017
- A Systematic Literature Review: Code Bad Smells in Java Source Code2017
- An Exploration of Code Quality in FOSS Projects2014
- An exploratory study of the impact of antipatterns on class change- and fault-pronenessEmpirical Software Engineering, 2011
- An Empirical Study of the Impact of Two Antipatterns, Blob and Spaghetti Code, on Program Comprehension2011
- Code churn: a measure for estimating the impact of code change2002
- The Robust Inference for the Cox Proportional Hazards ModelJournal of the American Statistical Association, 1989