## 2 Research Contexts
### Enhancement Path for Generating Dependency Code  
We have previously studied [8] the critical handlers of code generation solutions, summarizing various algorithms, architectures, tools, and strategies derived from machine translation and code analytics, which we consider to be the technical enablers. Drawing inspiration from these achievements, we attempt to enhance the intelligent generation model for dependency code. To be specific, we condense and combine the enablers of generating dependency code at different levels. These enablers include a dataset for determining the upper bound of the model, a usage scenario of a more user-friendly and innovative, technical environment for driving the model architecture, language characteristics of relative explicitness, and a usage pattern to be explored intensively. On this basis, we introduce an enhancement path shown in Figure 1, which comprehensively considers the research actuality, the inherent characteristics of dependency code, and the crucial handlers of the existing code generation framework and technology stack [8]. Foreseeable models for automatically generating dependency code fall into three different levels, namely data-driven models, language feature-based models and domain knowledge awareness models, paying close attention to five enablers that impact the model design. Therein, the content and quality of the dataset affect the scope of dependency code generation scenarios and the applicability of model algorithms. The popularity of open-source repositories, and the continuous development of technical environments, especially neural language models, have brought more feasibility to broadening the generation scenarios and introducing language characteristics and usage patterns. Overall, along the enhancement path, more powerful models take into account language features specific to dependency code, rather than textual information or essential source code. These three types of essence models reflect different model design priorities at various times and guide more practical directions for the ongoing enhancement of subsequent models. **Data-Driven Model. ** A classic recommendation model can easily identify the intuitive values and inherent relationships of the current dataset. Considering the technical environment at the time, researchers proposed data-driven models to discover or summarize potential relations according to their first impression. By omitting the additional domain knowledge, data-driven models pay scant attention to what kind of information the data contains, instead focusing on the latent state of the whole dataset, which is mostly derived from statistical-based algorithms. LibRec [4] is a typical example, which proposes a hybrid approach that combines association rule mining and collaborative filtering to recommend relevant libraries for developers. **Language Feature-based Model. ** The target language of this paper is the system configuration code, and the generation model is discussed with the dependency code as a typical example. However, the data-driven models are too straightforward to take into account the characteristics of the dependency code that are obviously different from the underlying data item. In this context, a dependency code is a particular source code, and the primary approach is launching intelligent exploration using the neural language model. With the release of brand-new datasets [6] and the continuous iteration of modeling algorithms [15; 16], in addition to recommending accompanying dependency libraries, it has become more practical and feasible to carry out intelligent exploration such as natural language-based dependency library generation. In terms of source code modeling, the integration of syntactic and semantic level information has proven to be effective in capturing and learning more features [17; 18]. In contrast to essential source code, a dependency code exhibits its peculiarities, which are also suitable for a system configuration code.