Self-admitted technical debt in R: detection and causes
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".