A method for re‐planning of software releases using discrete‐event simulation
Bibliographic record
Abstract
Abstract Software release planning can be described as a process consisting of the following three phases: (i) strategic release planning, i.e. the assignment of features to subsequent releases, (ii) operational release planning, i.e. the allocation of resources to tasks within each individual release, and (iii) dynamic re‐planning, i.e. the revision of plans to handle unexpected changes imposed on product/project managers responsible for the realization of individual releases. Example changes include the addition or removal of features and/or developers, adjustments due to over‐estimated developer productivity, or under‐estimated work volume of feature‐specific tasks, and adjusted degrees of task dependencies. The research presented in this article mainly focuses on phase (iii), in conjunction with phase (ii), of the release planning process, assuming that phase (i) has already been completed. For that purpose, we present a hybrid intelligence decision‐support method PRP (Planning/Re‐planning), and as its integral part a discrete‐event simulation model called DynaReP (Dynamic Re‐planner). The applicability, effectiveness, and efficiency of the proposed method and model are illustrated through a series of typical release planning and re‐planning scenarios on operational level. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".