Personalizing LLM-Based Conversational Programming Assistants
TLDR
This paper explores personalizing LLM-based conversational programming assistants to better meet diverse developer needs by considering cognitive and organizational factors.
Key contributions
- Characterizes how diverse developer cognition impacts needs for LLM assistants.
- Analyzes how organizational context shapes requirements for conversational programming tools.
- Explores personalization as a method to improve inclusivity of LLM-based assistants.
Why it matters
LLM-based programming assistants are powerful but struggle to meet diverse developer needs. This paper addresses this by exploring personalization, considering cognitive and organizational factors. It's crucial for making these tools inclusive and effective for all users, paving the way for more adaptable SE tools.
Original 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.
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