Release Planning Patterns for the Automotive Domain
Bibliographic record
Abstract
Context: Today’s vehicle development is focusing more and more on handling the vast amount of software and hardware inside the vehicle. The resulting planning and development of the software especially confronts original equipment manufacturers (OEMs) with major challenges that have to be mastered. This makes effective and efficient release planning that provides the development scope in the required quality even more important. In addition, the OEMs have to deal with boundary conditions given by the OEM itself and the standards as well as legislation the software and hardware have to conform to. Release planning is a key activity for successfully developing vehicles. Objective: The aim of this work is to introduce release planning patterns to simplify the release planning of software and hardware installed in a vehicle. Method: We followed a pattern identification process that was conducted at Dr. Ing. h. c. F. Porsche AG. Results: We introduce eight release planning patterns, which both address the fixed boundary conditions and structure the actual planning content of a release plan. The patterns address an automotive context and have been developed from a hardware and software point of view based on two examples from the case company. Conclusions: The presented patterns address recurring problems in an automotive context and are based on real life examples. The gathered knowledge can be used for further application in practice and related domains.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".