Enhanced Compiler Design with Machine Learning Models Integrated into Communicating Stream X-Machine Framework

Abstract

The increasing complexity of modern software systems demands more intelligent and reliable compiler designs capable of detecting code anomalies efficiently. Traditional compilers, while effective in syntax and semantic analysis, lack adaptive capabilities for identifying non-trivial errors. This paper presents a performance-aware hybrid compiler that integrates the Communicating Stream X-Machine (CSXM) formal framework with Machine Learning (ML) techniques for intelligent code analysis. The approach combines formal verification with predictive models, including Random Forest, Convolutional Neural Networks (CNN), and CNN-LSTM architectures. Experimental evaluation was conducted using both real-world (DeepFix) and synthetically generated datasets, with over 150 training and testing iterations. Performance was assessed using accuracy, precision, recall, and F1-score. In addition, this study provides a detailed analysis of compilation latency and ML inference overhead, highlighting the trade-off between detection accuracy and execution efficiency. Results show that ML integration significantly improves anomaly detection but introduces measurable latency. The paper also discusses practical challenges, including dataset labelling effort and model generalisability. These findings demonstrate that a performance-aware hybrid CSXM–ML compiler offers a promising approach for developing intelligent, adaptive compilation systems that balance correctness, efficiency, and scalability.