Theoretical framework for AI-driven code generation and summarization: Advancing software development through conceptual design principles

The recent advancement in the field of artificial intelligence (AI) has had a great impact on several fields, in particular software development and evolution. These advancements hold the promise of automating tasks that usually require significant time and expertise. This paper presents a foundation framework for software creation and evolution using a two-step toolchain by automating the development of source code, intended to improve software quality and development efficiency. The proposed framework seeks to minimize the time and effort required to understand and change software, therefore helping developers in managing complex or legacy systems. With a focus on automating the development of source code from natural language descriptions and summarizing existing codebases, the toolchain conceptually aims at utilizing the design of transformers. This paper explores the potential of transformers in automating the tasks of code generation and code summarization. The research looks into the potential effect of this toolchain on different phases of the software development, including requirements engineering, design, implementation, testing, and maintenance. Considering the possibilities of enhancing developer efficiency and software quality, even if the discussion is hypothetical, the proposed framework offers a base for future research and improvement in AI-powered software development tools.