Using real options to manage Technical Debt in Requirements Engineering
Bibliographic record
Abstract
Despite the importance of Requirements Engineering (RE) for the success of software products, most of the requirements decisions such as requirements specification and prioritization are still ad hoc and depend upon the managers' preferences and the trade-offs they make. The Technical Debt (TD) metaphor looks into the trade-offs between short term and long-term goals in software development projects that may lead to increased cost in the future. This problem is mainly due to the lack of a systematic and well-defined approach to manage the high level of uncertainty in requirements decisions. In this paper, we propose to apply the real options thinking to develop a quantitative method for managing requirements decisions under uncertainty and, more specifically for managing requirements debt in software development projects. A real option is a right without an obligation to make a specific future decision depending on how uncertainty resolves. We demonstrate the application of real options in the context of requirements debt valuation by using the binomial model combined with dynamic programming. We provide an illustrative example to show how uncertainty creates option value and influences requirements decisions and finally outline a future research agenda.
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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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".