Assuring Correctness, Testing, and Verification of Communicating Stream X-Machine (CSXM)-Based Compiler by Integrating Machine Learning Models

Traditional compilers face challenges in detecting and correcting complex errors due to their reliance on static rule-based methods. This research introduces an innovative hybrid approach that integrates Machine Learning (ML) models with a Communicating Stream X-Machine (CSXM)-based compiler to improve error detection and possible suggestion. The CSXM formal method is combined with advanced ML techniques, including Random Forest (RF), Convolutional Neural Network (CNN), and an optimized CNN-LSTM model, leveraging temporal dependencies for improved performance. The ML-enhanced compiler was trained and evaluated on the DeepFix and Generated datasets using metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that the CNN-LSTM outperformed RF and CNN across both datasets, achieving an accuracy of 89.72% on DeepFix and 90.00% on the Generated dataset, with minimal performance variance. This research demonstrates the potential of AI-driven compilers, providing a foundation for future exploration of intelligent, adaptive, and efficient compilation processes, and setting a benchmark for hybrid approaches in software engineering.