Sharma, RishabShahbazi, RaminFard, Fatemeh H.Codabux, ZadiaVidoni, Melina2023-12-042023-12-041573-7535http://hdl.handle.net/1885/307635Self-Admitted Technical Debt (SATD) is primarily studied in Object-Oriented (OO) languages and traditionally commercial software. However, scientifc software coded in dynamically-typed languages such as R difers in paradigm, and the source code comments’ semantics are diferent (i.e., more aligned with algorithms and statistics when compared to traditional software). Additionally, many Software Engineering topics are understudied in scientifc software development, with SATD detection remaining a challenge for this domain. This gap adds complexity since prior works determined SATD in scientifc software does not adjust to many of the keywords identifed for OO SATD, possibly hindering its automated detection. Therefore, we investigated how classifcation models (traditional machine learning, deep neural networks, and deep neural Pre-Trained Language Models (PTMs)) automatically detect SATD in R packages. This study aims to study the capabilities of these models to classify diferent TD types in this domain and manually analyze the causes of each in a representative sample. Our results show that PTMs (i.e., RoBERTa) outperform other models and work well when the number of comments labelled as a particular SATD type has low occurrences. We also found that some SATD types are more challenging to detect. We manually identifed sixteen causes, including eight new causes detected by our study. The most common cause was failure to remember, in agreement with previous studies. These fndings will help the R package authors automatically identify SATD in their source code and improve their code quality. In the future, checklists for R developers can also be developed by scientifc communities such as rOpenSci to guarantee a higher quality of packages before submissionThis study is partly supported by the Natural Sciences and Engineering Research Council of Canada, RGPIN-2021-04232 and DGECR-2021-00283 at the University of Saskatchewan, and RGPIN-2019-05175 at the University of British Columbia. Open Access funding enabled and organized by CAUL and its Member Institutions.application/pdfen-AU© The Author(s) 2022https://creativecommons.org/licenses/by/4.0/Self-admitted technical debtR packagesMachine learningDeep learningDeep neural pre-trained language models2022-08-2510.1007/s10515-022-00358-62022-08-28Creative Commons Attribution 4.0 International License