Self-admitted technical debt in R: detection and causes

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Abstract
Self-Admitted Technical Debt (SATD) is primarily studied in Object-Oriented (OO) languages and traditionally commercial software. However, scientific software coded in dynamically-typed languages such as R differs in paradigm, and the source code comments’ semantics are different (i.e., more aligned with algorithms and statistics when compared to traditional software). Additionally, many Software Engineering topics are understudied in scientific software development, with SATD detection remaining a challenge for this domain. This gap adds complexity since prior works determined SATD in scientific software does not adjust to many of the keywords identified for OO SATD, possibly hindering its automated detection. Therefore, we investigated how classification 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 different 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 identified sixteen causes, including eight new causes detected by our study. The most common cause was failure to remember, in agreement with previous studies. These findings 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 scientific communities such as rOpenSci to guarantee a higher quality of packages before submission.
Funding Information
  • University of British Columbia (RGPIN-2019-05175)
  • University of Saskatchewan (RGPIN-2021-04232, DGECR-2021-00283)
  • Australian National University

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