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

2025-08-06

AI Summary

Prior research lacks systematic investigation into leveraging large language models (LLMs) for object-oriented programming (OOP), particularly in pedagogical and industrial contexts. Method: This paper proposes a multi-perspective (novice, practitioner, expert) AI-assisted OOP framework integrating task decomposition, object-oriented semantic–aware code generation, and logical consistency verification—establishing, for the first time, a closed-loop reasoning–generation paradigm that aligns LLM capabilities with OOP principles. Contribution/Results: Empirical evaluation demonstrates significant improvements in code correctness, class design quality, and learning efficiency. We further introduce a novel, quantifiable evaluation metric suite for AI-assisted programming. This work advances the practical deployment of LLMs in structured programming paradigms and provides both theoretical foundations and actionable design patterns for intelligent programming education and industrial-grade OOP development tools.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Logic Programming

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Exploring LLMs' potential in Object-Oriented Programming tasks
• Enhancing OOP learning and code writing with AI tools
• Evaluating AI-powered tools for logical reasoning in coding

Innovation

• LLMs enhance OOP learning and coding
• Identify key workflow junctures for LLMs
• Augment logical reasoning and code writing