Performing operational release planning, replanning and risk analysis using a system dynamics simulation model
Bibliographic record
Abstract
Abstract Software release planning takes place on strategic and operational levels. Strategic release planning aims at assigning features to subsequent releases such that technical, resource, risk and budget constraints are met. Operational release planning focuses on the development of a single software release. Its purpose is to assign resources to feature development tasks such that total release duration is minimized under given process and project constraints. Replanning becomes necessary on the operational level because of addition or deletion of features during release development, due to changes in the workforce, or due to changes in process and project constraints. The allocation of resources to feature development tasks depends on the accurate estimation of planning parameters like task size, developer productivity or dependencies between task types. Risk analysis can help assess the reliability of a chosen release plan due to variation in these dependencies. In this article, we present elements of a simulation‐based methodology to planning, replanning and risk analysis of software releases on an operational level. Even though there exist approaches addressing these three aspects individually, our proposed approach combines all of them into one single package and, hence, offers stronger support to decision makers. The core element of the methodology is the process simulation model REPSIM‐2 (Release Plan Simulator, Version 2). We describe the functionality of REPSIM‐2 and illustrate its usefulness for planning, replanning and risk analysis through application scenarios. 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".