Supporting the Dynamic Reprioritization of Requirements in Agile Development of Software Products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Agile requirements engineering is the approach of choice for many software producers whose realities include highly uncertain requirements, use of new development technology, and clients willing to explore the ways in which an evolving product can help their business goals. From customer's perspective, the activity of continuous requirements reprioritization forms the very core of today's agile approaches. However, the freedom for clients to do so does not come for free. This paper presents results of a literature review on agile requirements prioritization methods, derives a conceptual model for understanding the inter-iteration prioritization process in terms of inputs and outcomes, and identifies issues and solutions pertinent to agile prioritization. The latter are derived from the authors' experiences and by using empirical data, published earlier by other authors.
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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) | 0.000 | 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 it