Trade-off Decisions Across Time in Technical Debt Management:A Systematic Literature Review
Bibliographic record
Abstract
Technical Debt arises from decisions that favour short-term outcomes at the cost of longer-term disadvantages. They may be taken knowingly or based on missing or incomplete awareness of the costs; they are taken in different roles, situations, stages and ways. Whatever technical or business factor motivate such decisions, they always imply a trade-off in time, a ‘now vs. later’. How exactly are such decisions made, and how have they been studied? This paper analyzes how decisions on technical debt are studied in software engineering via a systematic literature review. It examines the presently published Software Engineering research on Technical Debt, with a particular focus on decisions involving time. The findings reveal surprising gaps in published work on empirical research in decision making. We observe that research has rarely studied how decisions are made, even in papers that focus on the decision process. Instead, most attention is focused on engineering measures and feeding them into an idealized decision making process. These findings lead to a set of recommendations for future empirical research on Technical Debt.
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 | 0.025 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".