Adaptive Quality Engineering Using Machine Learning for Dynamic and Distributed Software Architectures
DOI:
https://doi.org/10.64137/31079377/IJMSD-V2I1P106Keywords:
Adaptive Quality Engineering, Software Defect Prediction, Observability, Distributed Systems, Microservices, Machine Learning, Automated Testing, Runtime GovernanceAbstract
Dynamic and distributed software architectures have expanded the scope of quality engineering beyond periodic testing, post-release defect tracking, and manually curated reliability reviews. Contemporary systems evolve through frequent code commits, independently deployable services, rapidly changing dependency graphs, observability pipelines, cloud runtime policies, and security controls that interact in non-linear ways. This paper proposes an adaptive quality engineering framework that integrates machine learning, observability, automated testing, governance controls, and runtime feedback to continuously predict, diagnose, and mitigate quality risks in distributed software ecosystems. Rather than treating quality assurance as a terminal activity performed after implementation, the proposed approach operationalizes quality as a closed loop that begins with design-time context, learns from development and production telemetry, and adapts verification priorities, test depth, rollout policies, and remediation workflows in real time. The framework combines multi-source quality signals from code, commits, service graphs, metrics, traces, logs, security events, and release metadata; transforms these into layered representations for defect prediction, failure propagation analysis, anomaly detection, and risk scoring; and uses policy-aware decision logic to trigger targeted quality actions. In addition to presenting the architectural layers and learning workflow, the paper formalizes design goals, adaptation rules, and evaluation criteria suitable for large-scale enterprise environments. The result is a research-grounded reference model for adaptive quality engineering that aligns software defect prediction, observability, resiliency analysis, and governance into a unified quality intelligence capability for modern distributed systems.
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