Bibliographic record
Abstract
Throughout a software development life cycle, developers knowingly commit code that is either incomplete, requires rework, produces errors, or is a temporary workaround. Such incomplete or temporary workarounds are commonly referred to as 'technical debt'. Our experience indicates that self-admitted technical debt is common in software projects and may negatively impact software maintenance, however, to date very little is known about them. Therefore, in this paper, we use source-code comments in four large open source software projects-Eclipse, Chromium OS, Apache HTTP Server, and ArgoUML to identify self-admitted technical debt. Using the identified technical debt, we study 1) the amount of self-admitted technical debt found in these projects, 2) why this self-admitted technical debt was introduced into the software projects and 3) how likely is the self-admitted technical debt to be removed after their introduction. We find that the amount of self-admitted technical debt exists in 2.4%-31% of the files. Furthermore, we find that developers with higher experience tend to introduce most of the self-admitted technical debt and that time pressures and complexity of the code do not correlate with the amount of self-admitted technical debt. Lastly, although self-admitted technical debt is meant to be addressed or removed in the future, only between 26.3%-63.5% of self-admitted technical debt gets removed from projects after introduction.
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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.000 |
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".