Insights into System Failures: ML-Assisted Testing and Failure Models for Cyber-Physical Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional software testing techniques focus on discovering inputs that reveal failures in the system under test. However, such techniques often fall short of providing explanations for the underlying reasons of system failures. I have made efforts to employ simulation-based testing to provide interpretable feedback to the developers regarding the circum-stances of system failures. To achieve this, I first explore machine learning (ML)-assisted test generation techniques for building failure models. These techniques leverage ML models to enhance the effectiveness and efficiency of testing, either by predicting the test outputs or providing guidance through a reduced search space. Subsequently, I build failure models using interpretable machine learning models based on tests generated by the ML-assisted test generation algorithms. Such failure models generate a set of rules that developers can easily interpret. The systems for which I build failure models belong to the cyber-physical and network domains. In this doctoral symposium, I present my research and outline plans for the remainder of my Ph.D. study.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it