Stress Testing Control Loops in Cyber-physical Systems
Bibliographic record
Abstract
Cyber-physical Systems (CPSs) are often safety-critical and deployed in uncertain environments. Identifying scenarios where CPSs do not comply with requirements is fundamental but difficult due to the multidisciplinary nature of CPSs. We investigate the testing of control-based CPSs, where control and software engineers develop the software collaboratively. Control engineers make design assumptions during system development to leverage control theory and obtain guarantees on CPS behaviour. In the implemented system, however, such assumptions are not always satisfied, and their falsification can lead the loss of guarantees. We define stress testing of control-based CPSs as generating tests to falsify such design assumptions. We highlight different types of assumptions, focusing on the use of linearised physics models. To generate stress tests falsifying such assumptions, we leverage control theory to qualitatively characterise the input space of a control-based CPS. We propose a novel test parametrisation for control-based CPSs and use it with the input space characterisation to develop a stress testing approach. We evaluate our approach on three case study systems, including a drone, a continuous-current motor (in five configurations), and an aircraft. Our results show the effectiveness of the proposed testing approach in falsifying the design assumptions and highlighting the causes of assumption violations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".