Risk-driven Online Testing and Test Case Diversity Analysis for ML-enabled Critical Systems (Replication Package)
Bibliographic record
Abstract
This repository contains the replication package as well as obtained results for the study titled "Risk-driven Online Testing and Test Case Diversity Analysis for ML-enabled Critical Systems". These include simulation control script, simulation file, test output and evaluation scripts. Hint: [search algorithm] refers to ES, GA, SA or RS. For instance, cais_zmq_[search algorithm].py could be cais_zmq_ES.py or cais_zmq_GA.py etc. cais_scenario.ttt is the actual simulation file and is loaded into CoppeliaSim cais_zmq_[search algorithm].py is the control script for each search algorithm and connected via the ZMQ zmqRemoteApi package file clusterdata[search algorithm].csv contains the output from clustering all test evaluations for the respective algorithm result_evaluation.py is the evaluation script Distribution.pdf is related only to distribution of failed evaluations evaluation results.zip contains outcomes from test data analysis (grouped[0,1,2].pdf are boxplots for failed, false "positives" and passed evaluations respectively) README.md instructions to run replication package result_[search algorithm].txt all test evaluations per algorithm run_details_[search algorithm].csv objective function output and time taken for each test evaluation per algorithm vargha_delaney.py python module for Vargha Delaney effect size determination Note: Instructions to run the package can be found in README.md
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.148 | 0.074 |
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".