Generative AI for Object-Oriented Programming: Writing the Right Code and Reasoning the Right Logic

TL;DR

This paper explores how large language models can be integrated into object-oriented programming to improve code writing and logical reasoning, addressing a significant research gap in AI-assisted OOP workflows.

Contribution

It presents a vision for integrating LLMs into OOP tasks, identifying key workflow junctures and proposing methods to enhance logical reasoning and code generation.

Findings

Identifies critical points where LLMs can assist in OOP workflows

Proposes methods to augment logical reasoning in AI-assisted coding

Highlights the potential benefits for programmers and stakeholders

Abstract

We find ourselves in the midst of an explosion in artificial intelligence research, particularly with large language models (LLMs). These models have diverse applications spanning finance, commonsense knowledge graphs, medicine, and visual analysis. In the world of Object-Oriented Programming(OOP), a robust body of knowledge and methods has been developed for managing complex tasks through object-oriented thinking. However, the intersection of LLMs with OOP remains an underexplored territory. Empirically, we currently possess limited understanding of how LLMs can enhance the effectiveness of OOP learning and code writing, as well as how we can evaluate such AI-powered tools. Our work aims to address this gap by presenting a vision from the perspectives of key stakeholders involved in an OOP task: programmers, mariners, and experienced programmers. We identify critical junctures within typical coding workflows where the integration of LLMs can offer significant benefits. Furthermore, we propose ways to augment existing logical reasoning and code writing, ultimately enhancing the programming experience.