Predicting and quantifying the technical debt in cloud software engineering-Reference-Cited by-同舟云学术

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Predicting and quantifying the technical debt in cloud software engineering

Published:2014-12 Issue: Volume: Page:
ISSN:
Container-title:2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
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Author:

Skourletopoulos Georgios,Mavromoustakis Constandinos X.,Bahsoon Rami,Mastorakis George,Pallis Evangelos

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7016297/7033190/07033201.pdf?arnumber=7033201

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. Using deep temporal convolutional networks to just-in-time forecast technical debt principal;Journal of Systems and Software;2022-12

2. Forecasting technical debt evolution in software systems: an empirical study;Frontiers of Computer Science;2022-11-08

3. Technical Debt Forecasting from Source Code Using Temporal Convolutional Networks;Product-Focused Software Process Improvement;2022

4. Technical Debt predictive model through Temporal Convolutional Network;2021 International Joint Conference on Neural Networks (IJCNN);2021-07-18

5. A Clustering Approach Towards Cross-Project Technical Debt Forecasting;SN Computer Science;2021-01-07

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