Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring
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 practice, more complex nonlinear relationships exist between metrics. Moreover, most intermetric correlations form clusters rather than simple pairwise correlations. These clusters provide additional information and offer the possibility for optimization. In this paper, we address these issues by using Normalized Mutual Information (NMI) as a similarity measure to identify clusters of correlated metrics, without assuming any specific form for the metric relationships. We show how to apply the Wilcoxon Rank-Sum test on the entropy measures to detect errors in the system. We also present three diagnosis algorithms to locate faulty components: RatioScore, based on the Jaccard coefficient, SigScore, which incorporates knowledge of component dependencies, and BayesianScore, which uses Bayesian inference to assign a fault probability to each component. We evaluate our approach in the context of a complex enterprise application, and show that 1) stable, nonlinear correlations exist and can be captured with our approach; 2) we can detect a large fraction of faults with a low false positive rate (we detect up to 18 of the 22 faults we injected); and 3) we improve the diagnosis with our new diagnosis algorithms.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".