NetCheck: network diagnoses from blackbox traces
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
This paper introduces NetCheck, a tool designed to diagnose network problems in large and complex applications. NetCheck relies on blackbox tracing mechanisms, such as strace, to automatically collect sequences of network system call invocations generated by the application hosts. NetCheck performs its diagnosis by (1) totally ordering the distributed set of input traces, and by (2) utilizing a network model to identify points in the totally ordered execution where the traces deviated from expected network semantics.Our evaluation demonstrates that NetCheck is able to diagnose failures in popular and complex applications without relying on any application-or network-specific information. For instance, NetCheck correctly identified the existence of NAT devices, simultaneous network disconnection/ reconnection, and platform portability issues. In a more targeted evaluation, NetCheck correctly detects over 95% of the network problems we found from bug trackers of projects like Python, Apache, and Ruby. When applied to traces of faults reproduced in a live network, NetCheck identified the primary cause of the fault in 90% of the cases. Additionally, NetCheck is efficient and can process a GB-long trace in about 2 minutes.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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