Abstract

The increasing scale, complexity, and velocity of software development has made traditional static and dynamic code analysis insufficient for ensuring code quality, security, and maintainability. Large-scale Neural Language Models (NLMs), especially transformer-based architectures pretrained on vast corpora of source code, have emerged as a promising paradigm for intelligent code analysis. By learning semantic representations of code, these models enable sophisticated tasks such as bug detection, vulnerability prediction, code summarization, and automated refactoring. This paper investigates intelligent code analysis frameworks that leverage large-scale neural language models, exploring their theoretical foundations, model adaptations for code understanding, and integration into software engineering pipelines. A comprehensive literature review traces the evolution from classic static analysis to neural code representations. We propose a research methodology that encompasses data collection, model training/fine-tuning, evaluation metrics, and deployment strategies tailored for industrial environments. The study analyzes the advantages of NLM-driven code analysis—such as adaptability and cross-language generalization—alongside disadvantages including data bias, computational costs, and explainability challenges. Results from benchmark evaluations and real-world case studies demonstrate both performance gains and areas for improvement. The paper concludes with insights into model limitations, implications for software quality, and future research directions aimed at enhancing robustness, interpretability, and developer trust in intelligent code analysis frameworks