A large empirical assessment of the role of data balancing in machine-learning-based code smell detection-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

A large empirical assessment of the role of data balancing in machine-learning-based code smell detection

Published:2020-11 Issue: Volume:169 Page:110693
ISSN:0164-1212
Container-title:Journal of Systems and Software
language:en
Short-container-title:Journal of Systems and Software

Author:

Pecorelli Fabiano,Di Nucci Dario,De Roover Coen,De Lucia Andrea

Funder

Excellence of Science Project SECO-Assist, Belgium

European Commission

Publisher

Elsevier BV

Subject

Hardware and Architecture,Information Systems,Software

Reference88 articles.

1. An empirical study of the impact of two antipatterns, blob and spaghetti code, on program comprehension;Abbes,2011

2. Is better data better than better data miners?: on the benefits of tuning smote for defect prediction;Agrawal,2018

3. Experience report: evaluating the effectiveness of decision trees for detecting code smells;Amorim,2015

4. Code smells for model-view-controller architectures;Aniche;Empir. Softw. Eng.,2018

5. Recovering traceability links between code and documentation;Antoniol;IEEE Trans. Softw. Eng.,2002

Cited by 56 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. Enhancing cardiovascular risk assessment with advanced data balancing and domain knowledge-driven explainability;Expert Systems with Applications;2024-12

2. Revisiting Code Smell Severity Prioritization using learning to rank techniques;Expert Systems with Applications;2024-09

3. A Platform-Agnostic Framework for Automatically Identifying Performance Issue Reports With Heuristic Linguistic Patterns;IEEE Transactions on Software Engineering;2024-07

4. Early and Realistic Exploitability Prediction of Just-Disclosed Software Vulnerabilities: How Reliable Can It Be?;ACM Transactions on Software Engineering and Methodology;2024-06-27

5. Improving Code Smell Detection by Reducing Dimensionality Using Ensemble Feature Selection and Machine Learning;SN Computer Science;2024-06-25

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3