Using Software Categories for the Development of Generative Software

TL;DR

This paper introduces a semi-automatic approach using software categories to identify code parts suitable for generation in model-driven development, streamlining generator development and maintenance.

Contribution

It proposes a novel method leveraging software categories to systematically identify candidate code for generation, reducing manual effort and improving maintainability.

Findings

The approach effectively identifies code candidates for generation based on dependencies.

It simplifies the process of separating handwritten and generated code.

The method supports tool-assisted development of generative software.

Abstract

In model-driven development (MDD) software emerges by systematically transforming abstract models to concrete source code. Ideally, performing those transformations is to a large extent the task of code generators. One approach for developing a new code generator is to write a reference implementation and separate it into handwritten and generatable code. Typically, the generator developer manually performs this separation a process that is often time-consuming, labor-intensive, difficult to maintain and may produce more code than necessary. Software categories provide a way for separating code into designated parts with defined dependencies, for example, "Business Logic" code that may not directly use "Technical" code. This paper presents an approach that uses the concept of software categories to semi-automatically determine candidates for generated code. The main idea is to iteratively derive the categories for uncategorized code from the dependencies of categorized code. The candidates for generated or handwritten code finally are code parts belonging to specific (previously defined) categories. This approach helps the generator developer in finding candidates for generated code more easily and systematically than searching by hand and is a step towards tool-supported development of generative software.