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Deep code generation is a topic of deep learning for software engineering(DL4SE), which adopts neural models to generate code for the intendedfunctions. Since end-to-end neural methods lack domain knowledge and softwarehierarchy awareness, they tend to perform poorly w.r.t project-level tasks. Tosystematically explore the potential improvements of code generation, we let itparticipate in the whole top-down development from \emph{expressibles} to\emph{executables}, which is possible in limited scopes. In the process, itbenefits from massive samples, features, and knowledge. As the foundation, wesuggest building a taxonomy on code data, namely code taxonomy, leveraging thecategorization of code information. Moreover, we introduce a three-layersemantic pyramid (SP) to associate text data and code data. It identifies theinformation of different abstraction levels, and thus introduces the domainknowledge on development and reveals the hierarchy of software. Furthermore, wepropose a semantic pyramid framework (SPF) as the approach, focusing onsoftware of high modularity and low complexity. SPF divides the code generationprocess into stages and reserves spots for potential interactions. In addition,we conceived preliminary applications in software development to confirm theneuro-symbolic framework.

Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables