Personalized Coding Assistants Adapting Large Language Models to Individual Developer Styles

Abstract

Personalized coding assistants are changing the software development process using the potential of Large Language Models (LLMs) to offer individualized assistance to developers. These AI-powered tools are more than just basic code-suggestion functionalities by evolving according to the specific coding habits, preferences, and workflows of each developer. Through the inspection of the history of coding by the developer, interactions, and feedback, individualized assistants have the ability to provide context-driven suggestions that notably increase coding efficiency, minimize bugs, and facilitate overall effectiveness. This paper examines the possibility of LLM-backed personalized coding assistants and how exactly these tools could be customized according to the capabilities of developers in different levels. The research delves into recent developments in AI-based code assistants and explores how LLMs can be tailored to know a developer's preferences in real-time, hence making more meaningful suggestions for code completion, debugging, and other development activities.

Through a careful analysis of literature, we outline the advancements in developing systems that can adapt to developers' changing needs. In addition, we propose a method for measuring the effect of personalized assistants on developer performance. User experiment results indicate that personalized assistants can improve task completion times and code correctness by a considerable margin, with greater benefits for less experienced developers. Yet, the paper also addresses the challenges, such as data privacy ethical concerns, model explainability, and the constraints of existing AI models to comprehensively grasp multifaceted developer processes. Addressing these challenges, this paper adds to the discourse on how customized AI tools can redefine the future of software development, providing a more bespoke and streamlined method of coding support.

The results indicate that the future holds when personalized coding assistants, always learning from the interactions of developers, will become a part of contemporary development environments, increasing productivity while improving the developer's experience.