An exploratory study on the introduction and removal of different types of technical debt in deep learning frameworks-Reference-Cited by-同舟云学术

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An exploratory study on the introduction and removal of different types of technical debt in deep learning frameworks

Published:2021-02-15 Issue:2 Volume:26 Page:
ISSN:1382-3256
Container-title:Empirical Software Engineering
language:en
Short-container-title:Empir Software Eng

Author:

Liu Jiakun,Huang Qiao,Xia Xin,Shihab Emad,Lo David,Li Shanping

Publisher

Springer Science and Business Media LLC

Subject

Software

Link

http://link.springer.com/content/pdf/10.1007/s10664-020-09917-5.pdf

Reference57 articles.

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2. Alves NS, Ribeiro LF, Caires V, Mendes TS, Spínola RO (2014) Towards an ontology of terms on technical debt. In: Sixth international workshop on managing technical debt (MTD), 2014. IEEE, pp 1–7

3. Bavota G, Russo B (2016) A large-scale empirical study on self-admitted technical debt. In: IEEE/ACM 13th working conference on mining software repositories (MSR), 2016. IEEE, pp 315–326

4. Brown N, Cai Y, Guo Y, Kazman R, Kim M, Kruchten P, Lim E, MacCormack A, Nord R, Ozkaya I, et al. (2010) Managing technical debt in software-reliant systems. In: Proceedings of the FSE/SDP workshop on future of software engineering research. ACM, pp 47–52

5. Carpenter M (1997) Survival analysis: a self-learning text

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