DebtFree: minimizing labeling cost in self-admitted technical debt identification using semi-supervised learning-Reference-Cited by-同舟云学术

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DebtFree: minimizing labeling cost in self-admitted technical debt identification using semi-supervised learning

Published:2022-04-06 Issue:4 Volume:27 Page:
ISSN:1382-3256
Container-title:Empirical Software Engineering
language:en
Short-container-title:Empir Software Eng

Author:

Tu Huy^ORCID,Menzies Tim

Publisher

Springer Science and Business Media LLC

Subject

Software

Link

https://link.springer.com/content/pdf/10.1007/s10664-022-10121-w.pdf

Reference88 articles.

1. Agrawal A, Menzies T (2018) Is” better data” better than” better data miners”?. In: ICSE

2. Ali K, Lhoták O (2012) Application-only call graph construction. In: ECOOP

3. Alves NSR, Ribeiro LF, Caires V, Mendes TS, Spínola RO (2014) Towards an ontology of terms on technical debt. In: TechDebt

4. Amershi S, Begel A, Bird C, DeLine R, Gall H, Kamar E, Nagappan N, Nushi B, Zimmermann T (2019) Software engineering for machine learning: A case study. In: ICSE

5. Bavota G, Russo B (2016) A large-scale empirical study on self-admitted technical debt. In: MSR

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1. Technical Debt Monitoring Decision Making with Skin in the Game;ACM Transactions on Software Engineering and Methodology;2024-08-26

2. Self-Admitted Technical Debts Identification: How Far Are We?;2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER);2024-03-12

3. Anti-Laundering Approach for Bitcoin Transactions;2023 14th International Conference on Information and Communication Systems (ICICS);2023-11-21

4. Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health;ACM Transactions on Software Engineering and Methodology;2023-11-09

5. Detecting multi-type self-admitted technical debt with generative adversarial network-based neural networks;Information and Software Technology;2023-06

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