An Exploratory Study on Self-Admitted Technical Debt
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it