Application of dynamic symbolic execution and test coverage enhancement combined with artificial intelligence in large-scale web software systems

Aiming at the bottleneck problems of path explosion and low efficiency of constraint solving faced by dynamic symbolic execution (DSE) in large-scale Web software systems, this paper applies a DSE and test coverage enhancement method combined with artificial intelligence (AI). Firstly, an Actor-Critic dual network model is constructed based on the PPO (Proximal Policy Optimization) algorithm. The Actor network outputs the probability distribution of path selection, and the Critic network evaluates the value of path state. Secondly, the path priority sorting strategy is updated through the generalized advantage estimation and pruning mechanism, and the path depth threshold is gradually increased by combining curriculum learning. The key branch priority solving strategy is applied to optimize the efficiency of constraint solving. Finally, after each round of symbolic execution, the coverage increment is used as a reward signal to update the model parameters online and dynamically optimize the path exploration strategy. Experimental results show that this method significantly improves the efficiency of path exploration (the number of paths reaches 225 and 216, respectively) and test coverage (74.9% and 73.4%) on large-scale Web systems, with an average solution success rate of 79.8% and an average vulnerability detection rate of 88%. It has good vulnerability detection capabilities and resource adaptability, and provides an efficient and scalable new paradigm for automated testing of complex systems.