Heteroscedastic models to track relationships between management metrics
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
Modern software systems expose management metrics to help track their health. Recently, it was demonstrated that correlations among these metrics allow faults to be detected and their causes localized. In particular, linear regression models have been used to capture metric correlations. We show that for many pairs of correlated metrics in software systems, such as those based on Java Enterprise Edition (JavaEE), the variance of the predicted variable is not constant. This behaviour violates the assumptions of linear regression, and we show that these models may produce inaccurate results. In this paper, leveraging insight from the system behaviour, we employ an efficient variant of linear regression to capture the non-constant variance. We show that this variant captures metric correlations, while taking the changing residual variance into consideration. We explore potential causes underlying this behaviour, and we construct and validate our models using a realistic multi-tier enterprise application. Using a set of 50 fault-injection experiments, we show that we can detect all faults without any false alarm.
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 | 0.001 |
| 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