In industrial cyber-physical systems (CPS), maintaining large-scale, long-lived software stacks demands rapid retrieval of protocol-compatible and system-context-aware code segments to avoid costly downtime and safety issues. Existing code search methods lack system-aware reasoning and suffer from rigid semantic fusion, leading to fragmented understanding of intercomponent dependencies in industrial software. To tackle these pain points, this article proposes an industrial-oriented hierarchical feature and graph-based code search (HFGCS) framework with sample-aware hierarchical fusion and hub-centric heterogeneous graph reasoning, customized for CPS maintenance requirements. It integrates a dynamic layer aggregation (DLA) module for adaptive multigranularity semantic fusion (capturing syntax-to-logic features based on query intent) and an integrated hub context encoder (IHCE) module that constructs a heterogeneous CPS graph with a global hub node to propagate cross-component dependencies (control logic, communication protocols, sensor/actuator bindings, and runtime constraints). A learnable gating network dynamically balances these representations to achieve intent-aligned and system-consistent code retrieval. Extensive experiments on CodeSearchNet-C show HFGCS improves MRR by 7.3%–15.9% over state-of-the-art baselines with strong cross-backbone robustness. Industrial validation confirms it shortens development cycles, enhances efficiency and accuracy, and prevents protocol mismatches in safety-critical CPS software. Feasible for deployment with topology-aware reasoning, HFGCS serves as a scalable and reliable retrieval engine, providing high-quality compatible code references to underpin the stable operation of industrial CPS and aligning with the core scope of Industrial Informatics. The code and data used in our study are available at online.

ABSTRACT As a pivotal technology for enhancing software development efficiency, research on code search based on deep learning has emerged as a current hotspot. This review systematically deconstructs the entire code search process into four core stages: dataset construction, code preprocessing, heterogeneous representation model construction, and query expansion, while conducting an in‐depth analysis of the application status and challenges of deep learning technologies. In dataset construction, the Q and A pairs and C and D pairs relied on by deep learning models suffer from a lack of standardization. For example, CodeSearchNet exhibits insufficient cross‐lingual versatility, and CoDesc has incomplete noise filtering. During the code preprocessing stage, bottlenecks such as AST granularity selection and sequence information redundancy restrict the efficiency of feature extraction. Although the introduction of transformer and graph neural networks has optimized structural representation, a unified evaluation mechanism is lacking. In the research of heterogeneous representation models, while LSTM, CNN, and pretrained models (such as CodeBERT) effectively narrow the semantic gap, their cross‐domain search accuracy is insufficient. In terms of query expansion, deep learning‐based keyword expansion and intent completion methods struggle to capture users' real needs due to low semantic alignment accuracy. This review proposes, for the first time, a standardized dataset construction framework integrating multimodal data, a syntax‐semantic dual‐layer preprocessing evaluation mechanism, a cross‐domain transfer representation model, and a large language model‐driven intent dynamic expansion scheme. These contributions lay a theoretical foundation for the systematic development of code search technologies and provide cross‐task methodological references for related fields such as code clone detection.