Two-Level Adaptation for Budget-Constrained Continuous Dynamic Dependence Analysis

Dynamic dependence analysis is essential for software performance optimization, debugging, regression testing, and security analysis. In long-running and distributed systems, continuously performing this analysis while balancing cost and precision under strict time budgets is a persistent challenge. Current state-of-the-art (SOTA) approaches tackled this problem but suffer from inefficient budget utilization and/or imprecise dependence results, limiting their practicality in real-world software engineering workflows. We introduce GDist, a novel two-level adaptation framework that self-tunes analysis parameters to optimize cost-effectiveness for continuous dynamic dependence analysis. GDist integrates a decision-tree-based learning model, tailored to system executions, with domain knowledge about analysis precision levels, ensuring a more precise and budget-observing adaptation strategy. We evaluate GDist on 12 real-world distributed systems and in two key applications: regression test reduction and vulnerability detection. Results show that GDist improves budget utilization by 29% and precision by 18% over SOTA. Specifically, GDist reduces regression testing costs by 31% while maintaining test effectiveness and lowers the cost of identifying true-positive vulnerabilities by 36% in enterprise-scale systems. Its lightweight adaptation and the inherent interpretability of decision trees make it well-suited for scalable, cost-aware software maintenance and security analysis. These merits position GDist as a practical and adaptive solution for modern software engineering challenges.