The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development (2302.07080v1)

Abstract: LLMs have recently been applied in software engineering to perform tasks such as translating code between programming languages, generating code from natural language, and autocompleting code as it is being written. When used within development tools, these systems typically treat each model invocation independently from all previous invocations, and only a specific limited functionality is exposed within the user interface. This approach to user interaction misses an opportunity for users to more deeply engage with the model by having the context of their previous interactions, as well as the context of their code, inform the model's responses. We developed a prototype system -- the Programmer's Assistant -- in order to explore the utility of conversational interactions grounded in code, as well as software engineers' receptiveness to the idea of conversing with, rather than invoking, a code-fluent LLM. Through an evaluation with 42 participants with varied levels of programming experience, we found that our system was capable of conducting extended, multi-turn discussions, and that it enabled additional knowledge and capabilities beyond code generation to emerge from the LLM. Despite skeptical initial expectations for conversational programming assistance, participants were impressed by the breadth of the assistant's capabilities, the quality of its responses, and its potential for improving their productivity. Our work demonstrates the unique potential of conversational interactions with LLMs for co-creative processes like software development.

Understanding the Capabilities of Conversational LLMs in Software Development

The paper "The Programmer's Assistant: Conversational Interaction with a LLM for Software Development" explores the application of conversational interfaces in software engineering, leveraging LLMs like Codex to facilitate extended interactions grounded in the user’s code context. Unlike conventional application of LLMs in development tools which treat model invocations independently, the paper introduces a prototype system—the Programmer's Assistant—that enables conversational exchanges to assist software engineers in a co-creative process.

Methodology and Implementation

The authors designed the Programmer's Assistant by embedding the Codex LLM into a code editor combined with a chat interface. This system allows users to engage in natural language interactions with the LLM, facilitating tasks such as generating code snippets, explaining code functions, answering programming queries, and performing additional tasks. The assistant's capability to remember context from prior exchanges distinguishes it from non-conversational models, as it supports more nuanced and context-aware interactions with users. The paper engaged 42 participants from varied roles and experiences, providing an empirical evaluation of the system over a tutorial and four programming challenges centered around code writing, documentation generation, and unit test creation.

Key Findings

High User Satisfaction

Numerical results from the user paper showed a significant increase in user satisfaction after interacting with the Programmer's Assistant compared to initial expectations. With an acceptance rate of 66.3% towards generated code snippets, the paper demonstrates the assistant’s effectiveness in real software development tasks.

Diverse Interaction and Role Perception

Participants exhibited varied interaction styles, balancing between using the assistant as a tool versus a collaborative agent. Despite expressing initial skepticism typical of conversational agents, many users indicated a desire to integrate such assistants into their regular programming workflow.

Enhanced Productivity and Learning

The ability of conversational LLMs to facilitate rapid acquisition of programming knowledge and aid in productivity was highlighted. The paper found that participants valued the assistant for its ability to recall previous interactions and leverage the code's context for more refined assistance, a critical capability for enhancing learning and efficiency in software development tasks.

Implications and Future Directions

The implications of this research are twofold. Practically, integrating conversational interfaces into development environments offers potential productivity boosts by allowing code context-dependent dialogues that improve interaction quality. Theoretically, it opens up new avenues for exploring AI-human collaboration through mutual learning frameworks, raising questions of AI adaptability, model improvement through user interactions, and alignment to user preferences and expectations.

Future developments in conversational AI for programming might explore personalization to individual developer needs, proactive agent behaviors, and more complex models integrating reasoning abilities beyond syntactical code manipulation. This could enhance human-AI synergy, making AI not just a tool but a partner in creative programming tasks.

In sum, the paper presents a compelling case for conversational LLMs in software engineering, underscoring their potential to transform workflows, democratize coding skills across experience levels, and cultivate user-friendly AI partnerships in creative technical domains.