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 \nat the cost of longer-term disadvantages. They may be taken \nknowingly or based on missing or incomplete awareness of the \ncosts; they are taken in different roles, situations, stages and ways. \nWhatever technical or business factor motivate such decisions, they \nalways imply a trade-off in time, a ‘now vs. later’. How exactly are \nsuch decisions made, and how have they been studied? \nThis paper analyzes how decisions on technical debt are studied \nin software engineering via a systematic literature review. It examines \nthe presently published Software Engineering research on \nTechnical Debt, with a particular focus on decisions involving time. \nThe findings reveal surprising gaps in published work on empirical \nresearch in decision making. We observe that research has rarely \nstudied how decisions are made, even in papers that focus on the \ndecision process. Instead, most attention is focused on engineering \nmeasures and feeding them into an idealized decision making process. \nThese findings lead to a set of recommendations for future \nempirical research on Technical Debt.
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How this classification was reachedexpand
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) | 0.001 | 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) | 0.116 | 0.011 |
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; both teacher heads agree on what is shown here.
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".