Personalizing LLM-Based Conversational Programming Assistants

TL;DR

This paper explores how personalization can improve LLM-based conversational programming assistants by addressing diverse developer needs and organizational contexts.

Contribution

It investigates the impact of cognitive and organizational diversity on developer needs and proposes personalization to enhance inclusivity of these assistants.

Findings

Personalization can potentially improve assistant effectiveness for diverse users.

Understanding developer diversity is crucial for tailoring conversational tools.

Future work aims to characterize and address varied developer needs.

Abstract

Large Language Models (LLMs) have shown much promise in powering a variety of software engineering (SE) tools. Offering natural language as an intuitive interaction mechanism, LLMs have recently been employed as conversational ``programming assistants'' capable of supporting several SE activities simultaneously. As with any SE tool, it is crucial that these assistants effectively meet developers' needs. Recent studies have shown addressing this challenge is complicated by the variety in developers' needs, and the ambiguous and unbounded nature of conversational interaction. This paper discusses our current and future work towards characterizing how diversity in cognition and organizational context impacts developers' needs, and exploring personalization as a means of improving the inclusivity of LLM-based conversational programming assistants.