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23 shades of self-admitted technical debt: an empirical study on machine learning software
Research article (Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2022) · cited 29× · AI/ML
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APA4ort.xyz Knowledge Graph. (2026). 23 shades of self-admitted technical debt: an empirical study on machine learning software. Retrieved May 24, 2026, from https://4ort.xyz/entity/23-shades-of-self-admitted-technical-debt-an-empirical-study-on-machine-learning-software
MLA“23 shades of self-admitted technical debt: an empirical study on machine learning software.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/23-shades-of-self-admitted-technical-debt-an-empirical-study-on-machine-learning-software.
BibTeX@misc{4ortxyz_23-shades-of-self-admitted-technical-debt-an-empirical-study-on-machine-learning-software_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{23 shades of self-admitted technical debt: an empirical study on machine learning software}}, year = {2026}, url = {https://4ort.xyz/entity/23-shades-of-self-admitted-technical-debt-an-empirical-study-on-machine-learning-software}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): 23 shades of self-admitted technical debt: an empirical study on machine learning software — https://4ort.xyz/entity/23-shades-of-self-admitted-technical-debt-an-empirical-study-on-machine-learning-software (retrieved 2026-05-24)
