Adaptive Deep Reinforcement Learning-Driven Test Case Prioritization for Highly Volatile Software Environments

Test execution needs change frequently due to software updates, making fixed test case prioritization unreliable. Unnecessary test runs and delays in finding faults occur because static methods do not adjust when new defects appear. Reordering test cases dynamically based on changing conditions can improve prioritization. A model that updates test rankings using past execution patterns, uncertainty estimation, and retraining triggers is introduced in this study as Adaptive Deep Reinforcement Learning-Driven Test Case Prioritization (ADRL-TCP). Long Short-Term Memory (LSTM) combined with a Double Deep Q-Network (DDQN) stores test effectiveness across multiple cycles. Test cases are ranked based on their likelihood of revealing failures using Bayesian uncertainty estimation. Prioritization is updated without restarting the entire learning process through incremental retraining. In continuous integration environments, the model is tested and compared to Reinforcement Learning for Test Case Prioritization (RL-TCP) and Prioritized Experience Replay Based on Dynamics Priority (PERDP). Finding failures requires fewer test executions, and faults are detected earlier using ADRL-TCP. Past outcomes and recent changes influence test rankings due to the memory-based learning approach. High-risk test cases receive earlier execution because prioritization is adjusted based on uncertainty awareness. Reducing test overhead while maintaining fault detection consistency in continuous testing environments is possible through ADRL-TCP, which adjusts as software evolves. Risk-aware ranking, adaptive retraining, and learning-based memory integration improve test prioritization, as observed in these findings.