Detecting and quantifying different types of self-admitted technical Debt
Bibliographic record
Abstract
Technical Debt is a term that has been used to express non-optimal solutions during the development of software projects. These non optimal solutions are often shortcuts that allow the project to move faster in the short term, at the cost of increased maintenance in the future. To help alleviate the impact of technical debt, a number of studies focused on the detection of technical debt. More recently, our work shown that one possible source to detect technical debt is using source code comments, also referred to as self-admitted technical debt. However, what types of technical debt can be detected using source code comments remains as an open question. Therefore, in this paper we examine code comments to determine the different types of technical debt. First, we propose four simple filtering heuristics to eliminate comments that are not likely to contain technical debt. Second, we read through more than 33K comments, and we find that self-admitted technical debt can be classified into five main types - design debt, defect debt, documentation debt, requirement debt and test debt. The most common type of self-admitted technical debt is design debt, making up between 42% to 84% of the classified comments. Lastly, we make the classified dataset of more than 33K comments publicly available for the community as a way to encourage future research and the evolution of the technical debt landscape.
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 | 0.005 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 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) | 0.001 | 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".