Dependency-aware fault diagnosis with metric-correlation models in enterprise software 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
The normal operation of enterprise software systems can be modeled by stable correlations between various system metrics; errors are detected when some of these correlations fail to hold. The typical approach to diagnosis (i.e., pinpoint the faulty component) based on the correlation models is to use the Jaccard coefficient or some variant thereof, without reference to system structure, dependency data, or prior fault data. In this paper we demonstrate the intrinsic limitations of this approach, and propose a solution that mitigates these limitations. We assume knowledge of dependencies between components in the system, and take this information into account when analyzing the correlation models. We also propose the use of the Tanimoto coefficient instead of the Jaccard coefficient to assign anomaly scores to components. We evaluate our new algorithm with a Trade6-based test-bed. We show that we can find the faulty components within top-3 components with the highest anomaly score in four out of nine cases, while the prior method can only find one.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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