PICASSOS – Practical Applications of Automated Formal Methods to Safety Related Automotive Systems
Bibliographic record
Abstract
PICASSOS was a UK government funded programme to improve the ability of automotive supply chains to develop complex software-intensive systems with high safety assurance and at an acceptable cost. This was executed by a consortium of three universities and five companies including an automotive OEM and suppliers. Three major elements of the PICASSOS project were: use of automated model based verification technology utilising formal methods; application of this technology in the context of ISO 26262; and evaluation to measure the impact of this approach to inform key management decisions on the costs, benefits and risks of applying this technology on live projects. The project spanned system level design and software development. This was achieved by using a unified model based process incorporating SysML at the system level and using Simulink and Stateflow auto-coded into C at the software level. An ISO 26262 compliant development process based on those already used by the commercial partners was used as a baseline, and a modified process using formal methods was developed. Tools that are commercially available were used wherever possible, and technology demonstrators were generated within the programme for enhancement and eventual commercial sale subsequently. A number of trials were undertaken comparing these two processes during simulated development of Electric Vehicle based systems. The paper includes the results of one of the trials, showing that the formal methods-based approach found errors that were missed by a standard model-verification process at software unit level and showing how it can do so with reduced effort.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".