Temporal Discounting in Software Engineering: A Replication Study
Bibliographic record
Abstract
Background: Many decisions made in Software Engineering practices are intertemporal choices: trade-offs in time between closer options with potential short-term benefit and future options with potential long-term benefit. However, how software professionals make intertemporal decisions is not well understood. Aim: This paper investigates how shifting time frames influence preferences in software projects in relation to purposefully selected background factors. Method: We investigate temporal discounting by replicating a questionnaire-based observational study. The replication uses a changed-population and -experimenter design to increase the internal and external validity of the original results. Results: The results of this study confirm the occurrence of temporal discounting in samples of both professional and student participants from different countries and demonstrate strong variance in discounting between study participants. We found that professional experience influenced discounting. Participants with broader professional experience exhibited less discounting than those with narrower experience. Conclusions: The results provide strong empirical support for the relevance and importance of temporal discounting in SE and the urgency of targeted interdisciplinary research to explore the underlying mechanisms and their theoretical and practical implications. The results suggest that technical debt management could be improved by increasing the breadth of experience available for critical decisions with long-term impact. In addition, the present study provides a methodological basis for replicating temporal discounting studies in software engineering.
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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 | 0.039 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".