Personalizing LLM-Based Conversational Programming Assistants
For developers using LLM-based programming assistants, this work aims to improve tool inclusivity by personalizing interactions, but it is currently at a conceptual stage.
This paper addresses the challenge of varying developer needs in LLM-based conversational programming assistants, proposing personalization to improve inclusivity. No concrete results are reported as it discusses ongoing and future work.
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.