Design of an iterative adaptive method for volatility-aware test case prioritization in rapidly evolving software systems.

This work suggests an adaptive, deep reinforcement learning-driven framework having five integrated modules that address important facets of volatility-aware optimization to improve the effectiveness of Test Case Prioritization (TCP). To assign priority scores using temporal and contextual attention mechanisms within a dynamic graph, the Dual-Attention Temporal Graph Prioritization Network (DAT-GPN) uses historical execution logs and changing software modifications. The Reinforcement-Driven Volatility-Aware Clustered Prioritizer (RD-VACP) uses Q-learning agents to optimize execution order and remove redundancy while clustering test cases as per volatility metrics. With the addition of epistemic and aleatoric uncertainties to a multi-agent PPO structure, the Uncertainty-Regularized Multi-Agent PPO Scheduler (UR-MAPPO) improves policy stability in dynamic test scenarios. To assess hypothetical test results for risk-aware decision-making, the Counterfactual Impact Analysis Prioritizer (CIAP) uses structural causal inference. Lastly, to balance detection time, risk exposure, and resource consumption, the Multi-Objective Adaptive Ensemble Prioritization Framework (MO-AEPF) combines reinforcement, causal, and sequential learning. This framework provides a dependable and understandable TCP solution. Dual-attention graph modeling for contextual and temporal prioritization. For risk-sensitive, optimal execution, use reinforcement and causal learning. Multi-objective ensemble optimization for resource efficiency and balanced fault detection.