Detecting and Preventing Latent Risk Accumulation in High-Performance Software Systems

• URL: http://arxiv.org/abs/2510.03712v2
• Date: Wed, 22 Oct 2025 23:56:45 GMT
• Title: Detecting and Preventing Latent Risk Accumulation in High-Performance Software Systems
• Authors: Jahidul Arafat, Kh. M. Moniruzzaman, Shamim Hossain, Fariha Tasmin,
• Abstract summary: Cache achieving fragility hit rates can obscure bottlenecks until cache failures trigger 100x load amplification and 99% cascading collapse.<n>Current reliability engineering focuses on reactive incident response rather than proactive detection of optimization-induced vulnerabilities.<n>This paper presents the first comprehensive framework for systematic latent risk detection, prevention, and optimization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modern distributed systems employ aggressive optimization strategies that create latent risks - hidden vulnerabilities where exceptional performance masks catastrophic fragility when optimizations fail. Cache layers achieving 99% hit rates can obscure database bottlenecks until cache failures trigger 100x load amplification and cascading collapse. Current reliability engineering focuses on reactive incident response rather than proactive detection of optimization-induced vulnerabilities. This paper presents the first comprehensive framework for systematic latent risk detection, prevention, and optimization through integrated mathematical modeling, intelligent perturbation testing, and risk-aware performance optimization. We introduce the Latent Risk Index (LRI) that correlates strongly with incident severity (r=0.863, p<0.001), enabling predictive risk assessment. Our framework integrates three systems: HYDRA employing six optimization-aware perturbation strategies achieving 89.7% risk discovery rates, RAVEN providing continuous production monitoring with 92.9% precision and 93.8% recall across 1,748 scenarios, and APEX enabling risk-aware optimization maintaining 96.6% baseline performance while reducing latent risks by 59.2%. Evaluation across three testbed environments demonstrates strong statistical validation with large effect sizes (Cohen d>2.0) and exceptional reproducibility (r>0.92). Production deployment over 24 weeks shows 69.1% mean time to recovery reduction, 78.6% incident severity reduction, and 81 prevented incidents generating 1.44M USD average annual benefits with 3.2-month ROI. Our approach transforms reliability engineering from reactive incident management to proactive risk-aware optimization.

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