Integrating Observability, Defect Prediction, and Decision Intelligence for Reliable AI-Driven Software Systems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I6P108Keywords:
Observability, Software Defect Prediction, Decision Intelligence, AIOps, MLOps, Software Reliability, AI-Driven Systems, Software Quality AssuranceAbstract
AI-driven software systems have expanded the operational surface area of modern platforms by coupling conventional application logic with data pipelines, machine learning components, and continuously changing runtime environments. This expansion makes reliability a moving target. Traditional quality assurance and post-deployment monitoring remain necessary, but they are no longer sufficient when failures emerge from interactions among code defects, model drift, infrastructure volatility, feature-store inconsistencies, and delayed operational response. This paper develops an integrated conceptual framework that unifies observability, software defect prediction, and decision intelligence into a single reliability architecture for AI-driven software systems. The proposed perspective argues that these capabilities should not be treated as isolated disciplines. Observability provides high-fidelity runtime evidence, defect prediction offers anticipatory risk estimation before failures become customer-visible, and decision intelligence converts technical signals into prioritized actions, governance routines, and architecture-level trade-off decisions. Drawing on literature from AIOps, MLOps, software quality engineering, testing, trustworthy AI, and architecture-centric governance, the paper synthesizes current knowledge, identifies fragmentation across the lifecycle, and proposes a layered operating model spanning development, deployment, operations, and continuous improvement. The manuscript also outlines adoption patterns, organizational prerequisites, and research challenges relevant to enterprise-scale implementation. The resulting framework is intended to support more reliable, auditable, and adaptive AI-enabled software delivery in complex socio-technical environments.
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