Towards Realistic SATD Identification through Machine Learning Models: Ongoing Research and Preliminary Results-Reference-Cited by-同舟云学术

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Towards Realistic SATD Identification through Machine Learning Models: Ongoing Research and Preliminary Results

Published:2024-07-10 Issue: Volume: Page:675-676
ISSN:
Container-title:Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering
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Author:

Gama Eliakim¹^ORCID,Paixao Matheus¹^ORCID,Cortés Mariela I.¹^ORCID,Monteiro Lucas¹^ORCID

Affiliation:

1. State University of Ceará, Fortaleza, Brazil

Funder

Capes

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3663529.3663876

Reference6 articles.

1. Managing Technical Debt Using Intelligent Techniques - A Systematic Mapping Study

2. Ward Cunningham. The WyCash Portfolio Management System. In Addendum to the Proceedings on Object-oriented Programming Systems, Languages, and Applications (Addendum), pages 29–30, 1992.

3. Using Natural Language Processing to Automatically Detect Self-Admitted Technical Debt

4. Aniket Potdar and Emad Shihab. An Exploratory Study on Self-Admitted Technical Debt. In 30th IEEE International Conference on Software Maintenance and Evolution, Victoria, BC, Canada, September 29 - October 3, 2014, pages 91–100, 2014.

5. A tertiary study on technical debt: Types, management strategies, research trends, and base information for practitioners

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