登录 注册 会员服务 联系我们

Identifying self-admitted technical debt through code comment analysis with a contextualized vocabulary

  • Published:2020-05 Issue: Volume:121 Page:106270
  • ISSN:0950-5849
  • Container-title:Information and Software Technology
  • language:en
  • Short-container-title:Information and Software Technology

Author:

Farias Mário André de Freitas,Neto Manoel Gomes de MendonçaORCID,Kalinowski MarcosORCID,Spínola Rodrigo OliveiraORCID

Publisher

Elsevier BV

Subject

Computer Science Applications,Information Systems,Software

Reference40 articles.

1. Using natural language processing to automatically detect self-admitted technical debt;Maldonado;IEEE Trans. Softw. Eng.,2017

2. Managing technical debt in software engineering;Avgeriou;Dagstuhl Rep.,2016

3. The most common causes and effects of technical debt: first results from a global family of industrial surveys;Rios,2018

4. Towards an ontology of terms on technical debt;Alves,2014

5. Identification and management of technical debt: a systematic mapping study;Alves;Inf. Softw. Technol.,2016

Cited by 24 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. Technical Debt Tools: a Survey and an Empirical Evaluation;Journal of Software Engineering Research and Development;2024-08-19

2. Large language model ChatGPT versus small deep learning models for self‐admitted technical debt detection: Why not together?;Software: Practice and Experience;2024-06-28

3. What Can Self-Admitted Technical Debt Tell Us About Security? A Mixed-Methods Study;Proceedings of the 21st International Conference on Mining Software Repositories;2024-04-15

4. SATDAUG - A Balanced and Augmented Dataset for Detecting Self-Admitted Technical Debt;Proceedings of the 21st International Conference on Mining Software Repositories;2024-04-15

5. ICG: A Machine Learning Benchmark Dataset and Baselines for Inline Code Comments Generation Task;International Journal of Software Engineering and Knowledge Engineering;2023-10-20
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