Detecting and Preventing Latent Risk Accumulation in High-Performance Software Systems
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914108753838080 |
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| author | Arafat, Jahidul Moniruzzaman, Kh. M. Hossain, Shamim Tasmin, Fariha |
| author_facet | Arafat, Jahidul Moniruzzaman, Kh. M. Hossain, Shamim Tasmin, Fariha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03712 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting and Preventing Latent Risk Accumulation in High-Performance Software Systems Arafat, Jahidul Moniruzzaman, Kh. M. Hossain, Shamim Tasmin, Fariha Software Engineering 68M15, 90B25, 68T05, 90C29 C.4; C.2.4; D.2.5; D.4.5 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. |
| title | Detecting and Preventing Latent Risk Accumulation in High-Performance Software Systems |
| topic | Software Engineering 68M15, 90B25, 68T05, 90C29 C.4; C.2.4; D.2.5; D.4.5 |
| url | https://arxiv.org/abs/2510.03712 |