## 1 Introduction
While these existing enablers can be directly transferred into the dependency code generation model, they cannot be directly used to design the corresponding model based on dependency code features. Furthermore, exploring an enhancement path for the progressive generation model will promote dependency code generation systematically and feasibly. Our previous research [8] carried out a systematic review of natural language-based source code generation (NLSCG), proposed the meta-model and reference framework to assist in better understanding NLSCG problems, and finally dissected the ongoing technical enablers to promote NLSCG solutions into further research enhancements or production applications. The generic pipeline proposed by recent research [9] has clarified the significance and popularity of representation learning and domain knowledge when learning from programming languages, which also confirms the enhancement direction of this paper. Based on these fundamental frameworks, this paper performs a thorough analysis of system configuration code with the Maven dependency code as the representative instance, empirically investigates intelligent dependency code generation advances according to remarkable enablers, and discovers the self-consistent enhancement path that may stimulate potential improvements for dependency code generation. We analyze the enhancement path from three levels: a data-driven model, a language feature-based model, and a domain knowledge awareness model. Following the enhancement path, we address each challenge one by one and discover the blind spot that current studies did not uncover. Since current studies generally fail to cover the language feature and domain knowledge, we implement and evaluate the generation model concentrating on these two aspects. In summary, this paper makes the following contributions:  
1. We present an enhancement path to clarify the ongoing dependency code generation at different levels, introducing five enablers that drive the model design, including data sources, usage scenarios, technical environments, language characteristics, and usage patterns. 2. We propose EMGSCC (Enhanced Model for Generating System Configuration Code), a concrete generation model that mirrors the language feature introduction and domain knowledge integration simultaneously. EMGSCC learns from the dependency code from different open-source Maven projects, then customizes a neural language model with special consideration for the order-independent features and domain information to generate practical accompanying items. 3. We evaluate EMGSCC on the DDDI dataset to generate accompanying dependency libraries. By comparing with the baseline model and ablation experiments, EMGSCC demonstrates its effectiveness through improvements varying from 1% to 8% on all metrics. The remainder of this paper is as follows. Section 2 describes our study's research context and presents the enhancement path. We introduce the proposed approach EMGSCC in Section 3. Section 4 introduces the DDDI dataset, evaluation metrics, and experimental results. The probable working reason and threats to validity are discussed in Section 5. Section 6 enumerates the previous studies and techniques related to our problem. Finally, this paper is concluded in Section 7.