Resumen
Thanks to the widespread application of software frameworks, OTS components, DSLs, and new-generation software building and construction systems, leveraging system configuration code to assist in development is increasingly common, especially in agile software development. In software system implementation, the system configuration code is used to separate configuration items from the underlying logic, of which Maven dependency code is a typical example. To improve software productivity, developers often reuse existing dependency libraries. However, the large quantity, rapid iteration, and various application scenarios exacerbate the challenge for researchers to reuse the library efficiently and appropriately. Proper reuse of Maven dependencies requires correct importation, which is the research priority of this article; putting it into practical usage at the functional level is the next step. In order to overcome this barrier, researchers have proposed a number of recommendation and intelligent generation models based on deep learning algorithms and code learning strategies. We first introduce an enhancement path for the generation model in order to propose novel models that are more targeted than previous studies. We propose EMGSCC (Enhanced Model for Generating System Configuration Code), which generates accompanying dependency libraries based on the libraries already employed by the current system. EMGSCC uses content-based attention to cope with dependency language features and integrate additional domain information. Finally, we evaluate EMGSCC on the DDDI dataset with extra domain information, and findings show that improvement varies from 1% to 8% on all metrics compared with the baseline. We show empirical evidence of our enhancement path for generating system configuration code based on neural language models, and continuous improvement in this direction would yield promising results.