Automatic fault detection and diagnosis in complex software systems by information-theoretic monitoring
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
Management metrics of complex software systems exhibit stable correlations which can enable fault detection and diagnosis. Current approaches use specific analytic forms, typically linear, for modeling correlations. In this paper we use normalized mutual information as a similarity measure to identify clusters of correlated metrics, without knowing the specific form. We show how we can apply the Wilcoxon rank-sum test to identify anomalous behaviour. We present two diagnosis algorithms to locate faulty components: RatioScore, based on the Jaccard coefficient, and SigScore, which incorporates knowledge of component dependencies. We evaluate our mechanisms in the context of a complex enterprise application. Through fault injection experiments, we show that we can detect 17 out of 22 faults without any false positives. We diagnose the faulty component in the top five anomaly scores 7 times out of 17 using SigScore, which is 40% better than when system structure is ignored.
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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 | 0.000 |
| 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