## 3 Materials and Methods
### Design of Research Method's Framework  
The HCDMB model adopts a hierarchical and progressive three-level architecture design, which is composed of an input layer, MoE core layer and output layer (as shown in Figure 1).  
The input layer mainly realizes the dual functions of multi-dimensional software development behavior data processing and user intention recognition. By integrating multi-source heterogeneous data acquisition pipelines, the behavior data in the whole life cycle of software development are feature extraction and engineering processing. Specifically, the data sources include development behavior trajectories, collaborative and interaction records, system and application data. The feature engineering methods adopted are as follows: for unstructured text data (such as requirements documents, code development, etc.), the embedding models based on BAAI General Embedding (BGE) [34] and CodeBERT [35] are used to encode semantic vectors to build project knowledge bases. For temporal behavior characteristics (such as code submission frequency, test coverage, etc.), key information is extracted and summarized as descriptive text as supplementary knowledge. The input layer is the input data of the model by associating the user's intention with its multi-dimensional behavioral information.  
The core layer of the MoE constructs an expert collaborative decision-making system, which includes an expert model group and a routing gate network. The expert model group constructed by this method is configured with four domain specialization modules: requirements semantic parsing expert based on Deepseek v3, development code generation expert based on CodeGeeX4 [36], test optimization expert based on CodeGeeX4, and project deployment and orchestration strategy expert based on Deepseek v3. In the inference stage, each expert model accesses the project knowledge base through the retrieval augmented generation (RAG) method [37], and retrieves the knowledge information related to users'  
Figure 1: Design of research method’s framework.  
behavior data and intentions in the knowledge base as the context information input to each expert model to enhance and constrain the answers of the model. The routing gate network uses the Deepseek model as the base model, and uses the dynamic routing algorithm to realize the task decomposition and selection of expert models based on user intention and related behavioral information. For example, when high-frequency code commits are identified to be associated with test failures, the gated network will improve the decision weights of the test optimization experts, project deployment and orchestration experts, and trigger the collaborative response of automated test-case generation and team communication suggestions. At the same time, reinforcement learning from human feedback (RLHF) [38] is used to fine-tune the routing gate network and improve its effectiveness.  
The output layer fuses the expert opinions of the MoE core layer by constructing an analysis model and generates operational schemes, such as developing process optimization suggestions. The dynamic resource allocation scheme includes the strategic scheduling of computing resources (GPU instance allocation) and human resources (developer task-load balancing), as well as intelligent auxiliary output, covering code autocompletion, fault location and requirements tracking functions.