What is it about?

Deep code generation is a topic of deep learning for software engineering (DL4SE), which adopts neural models to generate code for the intended functions. Since end-to-end neural methods lack domain knowledge and software hierarchy awareness, they tend to perform poorly w.r.t project-level tasks. To systematically explore the potential improvements of code generation, we let it participate in the whole top-down development from expressibles to executables, which is possible in limited scopes. In the process, it benefits from massive samples, features, and knowledge.

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Why is it important?

As the foundation, we suggest building a taxonomy on code data, namely code taxonomy, leveraging the categorization of code information. Moreover, we introduce a three-layer semantic pyramid (SP) to associate text data and code data. It identifies the information of different abstraction levels, and thus introduces the domain knowledge on development and reveals the hierarchy of software. Furthermore, we propose a semantic pyramid framework (SPF) as the approach, focusing on software of high modularity and low complexity. SPF divides the code generation process into stages and reserves spots for potential interactions. In addition, we conceived preliminary applications in software development to confirm the neuro-symbolic framework.

Perspectives

I hope this paper serves as a reminder of the potential opportunities of applying state-of-the-art techniques such as neural models to software engineering. Especially when it comes to these common software applications, the time has come to automate the typical development process. The introduced mental models and frameworks are a general conception of how to proceed with this process. It is promising to consider further work along this direction, since emerging industrial tech-stacks are following the similar principles or approaches.

Jian Gu
Monash University

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This page is a summary of: Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables, November 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3611643.3613076.
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