Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality

2025-09-12

AI Summary

This study investigates the interaction dynamics between developers and large language models (LLMs) in real-world software development and their impact on code quality. We analyze CodeChat—the first large-scale, real-world dialogue dataset comprising over 360,000 code snippets across 20+ programming languages—using natural language processing, topic modeling, and static code analysis to characterize multi-turn dialogue patterns (68% multi-turn), task distributions, and defect evolution. We uncover language-specific error patterns for the first time: undefined variable rates reach 83.4% in Python and 75.3% in JavaScript, while Java exhibits a 75.9% comment omission rate. Empirically, feedback explicitly identifying errors and requesting fixes proves most effective—improving Java documentation quality by 14.7%. These findings provide an empirical foundation for enhancing the reliability of LLM-assisted programming and guiding the design of next-generation developer tools.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large Language Models (LLMs) are becoming integral to modern software development workflows, assisting developers with code generation, API explanation, and iterative problem-solving through natural language conversations. Despite widespread adoption, there is limited understanding of how developers interact with LLMs in practice and how these conversational dynamics influence task outcomes, code quality, and software engineering workflows. To address this, we leverage CodeChat, a large dataset comprising 82,845 real-world developer-LLM conversations, containing 368,506 code snippets generated across over 20 programming languages, derived from the WildChat dataset. We find that LLM responses are substantially longer than developer prompts, with a median token-length ratio of 14:1. Multi-turn conversations account for 68% of the dataset and often evolve due to shifting requirements, incomplete prompts, or clarification requests. Topic analysis identifies web design (9.6% of conversations) and neural network training (8.7% of conversations) as the most frequent LLM-assisted tasks. Evaluation across five languages (i.e., Python, JavaScript, C++, Java, and C#) reveals prevalent and language-specific issues in LLM-generated code: generated Python and JavaScript code often include undefined variables (83.4% and 75.3% of code snippets, respectively); Java code lacks required comments (75.9%); C++ code frequently omits headers (41.1%) and C# code shows unresolved namespaces (49.2%). During a conversation, syntax and import errors persist across turns; however, documentation quality in Java improves by up to 14.7%, and import handling in Python improves by 3.7% over 5 turns. Prompts that point out mistakes in code generated in prior turns and explicitly request a fix are most effective for resolving errors.

Problem

• Analyze real-world developer-LLM conversational interactions and dynamics
• Evaluate code quality issues in LLM-generated code across languages
• Identify effective prompt strategies for error resolution in conversations

Innovation

• Leveraged CodeChat dataset with 82,845 developer-LLM conversations
• Analyzed multi-turn conversations across 20 programming languages
• Identified error patterns and improvement strategies in generated code