Temporal Discounting in Technical Debt: How do Software Practitioners\n Discount the Future?
Bibliographic record
Abstract
Technical Debt management decisions always imply a trade-off among outcomes\nat different points in time. In such intertemporal choices, distant outcomes\nare often valued lower than close ones, a phenomenon known as temporal\ndiscounting. Technical Debt research largely develops prescriptive approaches\nfor how software engineers should make such decisions. Few have studied how\nthey actually make them. This leaves open central questions about how software\npractitioners make decisions.\n This paper investigates how software practitioners discount uncertain future\noutcomes and whether they exhibit temporal discounting. We adopt experimental\nmethods from intertemporal choice, an active area of research. We administered\nan online questionnaire to 33 developers from two companies in which we\npresented choices between developing a feature and making a longer-term\ninvestment in architecture. The results show wide-spread temporal discounting\nwith notable differences in individual behavior. The results are consistent\nwith similar studies in consumer behavior and raise a number of questions about\nthe causal factors that influence temporal discounting in software engineering.\nAs the first empirical study on intertemporal choice in SE, the paper\nestablishes an empirical basis for understanding how software developers\napproach intertemporal choice and provides a blueprint for future studies.\n
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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 | 0.010 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".