Proceedings of the First International Workshop on Automotive Software Architecture (WASA'15, Montreal, Canada, May 4, 2015)
Bibliographic record
Abstract
It is our great pleasure to welcome you to the First International Workshop on Automotive Software Architectures -- WASA'15. More than a decade ago, the term automotive software engineering was officially introduced in the software community addressing research challenges and technical issues encountering software development in the automotive domain. Today, vehicles are complex systems with millions of lines of code, dozens of microcontrollers, and intertwined networking system. Self driving cars, (fully) electric vehicles, Car-to-X communications are all enabled by software and new features require more advanced software architecture and engineering approaches suitable for automotive domain. The objective of the workshop is to become the premier forum for presentation of research results, industrial experience reports, and future trend discussions on the automotive software architecture field. WASA gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of the automotive software architecture field. The call for papers attracted submissions from Europe, Canada, and Brazil. We also encourage attendees to attend the keynote presentation. These valuable and insightful talk can and will guide us to a better understanding of the future: "SIMONE: Architecture-Sensitive Near-miss Clone Detection for Simulink Models", James Cordy (Queen's University).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.135 | 0.054 |
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".