Rigorous Support for Flexible Planning of Product Releases - A Stakeholder-Centric Approach and Its Initial Evaluation
Bibliographic record
Abstract
This paper addresses the problem of product release planning in iterative product development. We propose a method which combines decision, process, and tool support. The method, which is called SCERP, facilitates the active involvement of stakeholders in the different stages of the planning process. SCERP is flexible in the number of stakeholders involved, in the number of releases, in the number and definition of planning criteria, and in the selection of the best plan out of a set of optimized alternatives. A proof-of-concept of the method is given by a case study of release planning for a tool called Agilefant, which is developed with a process partially based on Scrum. The benefits of the method as demonstrated by the case study are: (i) better decisions by the product manager by relying on more objective information, (ii) more transparency of release decisions, and (iii) efficient tool support accompanying the whole process.
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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 | 0.054 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".