Context-Aware Predictive Optimization of Non-Functional Requirement Conflicts in DevOps Using Machine Learning

Abstract

Modern DevOps ecosystems operate in highly dynamic environments where multiple non-functional requirements (NFRs), such as performance, security, and reliability, must be satisfied simultaneously. However, these requirements often exhibit conflicting behaviors under varying runtime conditions, leading to degradation in system quality. Existing approaches primarily focus on either conflict detection or runtime optimization, with limited integration of predictive and adaptive mechanisms. This paper proposes a predictive optimization framework that integrates event log mining, cost-sensitive machine learning, and multi-objective optimization to proactively manage NFR conflicts in DevOps environments. The framework extracts contextual features from event logs and combines them with runtime monitoring and software repository metrics to construct a comprehensive feature space. A cost-sensitive Random Forest model is employed to predict conflict likelihood while prioritizing high-impact conflicts. Upon prediction, a multi-objective optimization module based on NSGA-II dynamically identifies optimal NFR operationalizations. Experimental evaluation using multi-source datasets demonstrates that the proposed framework achieves an accuracy of 92.4% and an AUC of 0.95, with an average conflict reduction of 41%. Statistical validation using paired t-tests confirms the significance of improvements (p < 0.05). These results indicate the effectiveness of integrating contextual intelligence with predictive modeling and optimization for proactive NFR conflict management in DevOps systems.