Stakeholder identification for a structured release planning approach in the automotive domain
Bibliographic record
Abstract
Abstract In regulated domains like automotive, release planning is a complex process. This complex process consists of an agreement between product development processes for hardware as well as mechanic systems and approaches for software development. Particularly in automotive, the creation and synchronization of release plans for hardware as well as software is a challenge. Within the whole complex system development, it is challenging to consider the relevant stakeholders in the initial creation of a release plan. Depending on the context that a release plan shall be created for, there are different stakeholders that have to be considered from the beginning. There are numerous publications in the area of release planning, but there is no detailed research that shows which stakeholders have to be addressed in the automotive context. The aim of this work is to identify stakeholders of a release plan as an appropriate approach to create transparency in release planning in the automotive domain. Action research to elaborate relevant stakeholders for release planning was conducted at Dr. Ing. h. c. F. Porsche AG. We present a detailed overview of identified stakeholders as well as their required content and added value regarding two pilot projects. With this contribution, identified stakeholders of release planning from the hardware and software points of view are introduced. We discuss, based on the results, why there are common stakeholders for the two projects and why there are individual stakeholders for each project. With this work, we present a more complete stakeholder identification and a more detailed understanding of their needs.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".