CodeChat Dataset Overview

• CodeChat dataset is a large-scale corpus of authentic developer–LLM interactions that captures multi-turn dialogues and iterative code refinement across 20+ programming languages.
• It comprises 82,845 conversations and 368,506 code snippets, enabling detailed empirical analysis of code defect trends and documentation practices.
• The dataset highlights effective corrective prompting and evolving error dynamics across dialogue turns, informing the design of next-generation conversational code assistants.

The CodeChat dataset is a large-scale corpus of developer–LLM interactions that encapsulates authentic, real-world code-related conversations. Derived from the WildChat dataset, CodeChat contains 82,845 developer–LLM conversations comprising 368,506 code snippets and spanning over 20 programming languages, with 68% of the interactions being multi-turn. This resource enables rigorous empirical analysis of the conversational dynamics between developers and LLMs—particularly the nature of iterative prompt refinement, code defect prevalence, and error resolution strategies—thus providing foundational material for research on conversational code assistants and LLM-based coding workflows (Zhong et al., 12 Sep 2025).

1. Dataset Construction and Properties

CodeChat is sourced from public chatbot services, filtered to retain only those interactions consisting of code blocks denoted by Markdown triple backticks. Each conversation is a dialogue sequence, and the dataset collates 311,161 dialogue turns from 26,085 users, identified via hashed IPs, ensuring uniqueness without personalized tracking. Conversations span a broad set of programming languages, with focused analysis on Python, JavaScript, C++, Java, and C#. In contrast to curated or synthetic corpora, CodeChat comprises unedited, naturally occurring exchanges driven by actual developer queries and challenges.

A majority (68%) of dialog threads involve more than one developer–LLM turn, reflecting iterative clarification and problem-solving patterns. Each conversation includes both developer prompts and corresponding LLM responses, which are typically much longer—with median token-length ratios of 14:1 (LLM:developer), indicating verbose completions and detailed explanations or code samples.

2. Programming Language Coverage and Code Snippet Statistics

CodeChat supports empirical analysis across multiple programming languages; more than 20 are present, but the dataset's focused evaluation targets the five most frequent: Python, JavaScript, C++, Java, and C#. In total, 368,506 code snippets are distributed across the corpus. The dataset enables cross-language measurement of code quality characteristics, such as error types, documentation standards, and dependency handling.

Topic annotation reveals web design queries constitute 9.6% of conversations, and neural network training represents 8.7%, indicating that LLMs are used for both front-end and machine learning-related coding tasks.

3. Prevalence and Typology of Code Defects

Quantitative analysis uncovers high rates of language-specific issues in LLM-generated code:

• Python: Invalid naming and undefined variables appear in 83.4% of snippets (measured by Pylint checks); import errors and context loss also manifest repeatedly.
• JavaScript: Undefined variables are present in 75.3% of cases; syntax errors affect 14.4% of snippets.
• C++: Omission of required header files (e.g., missing #include) affects 41.1% of samples.
• Java: 75.9% of snippets lack mandatory comments and documentation.
• C#: Unresolved namespace errors appear in 49.2% of responses.

The metric for defect prevalence in turn $N$ is given by

$P_{m, n} = \frac{C_{m, n}}{C_n} \times 100$

where $C_{m, n}$ counts conversations with at least one defect $m$ in turn $n$, and $C_n$ is the total number of sequences with at least $n$ turns.

4. Multi-Turn Interactions: Iterative Refinement and Error Dynamics

Multi-turn conversations (68% of the corpus) are essential for emulating real-world code development workflows where developers incrementally clarify or broaden their requirements. The dataset exhibits several iterative phenomena:

• Python: Undefined variable issues increase over more turns (from 23.5% at turn 1 to 32.8% at turn 5), while import errors decrease modestly (from 48.3% to 44.6%), suggesting a nuanced dynamic between code context loss and dependency resolution.
• Java: Missing required comments drop from 78.1% at turn 1 to 63.4% at turn 5, indicating a 14.7% improvement in documentation quality over the course of a dialogue.
• Turnwise error persistence: Syntax and import errors persist across turns but are subject to incremental improvement when developers adopt corrective prompting strategies. This suggests that LLMs respond effectively to explicit, focused feedback on prior outputs.

A plausible implication is that multi-turn, context-aware prompt engineering can mitigate certain LLM code-generation shortcomings, particularly in documentation and dependency management.

5. Developer Prompting Strategies and Error Resolution

The dataset demonstrates that the most effective approach for error resolution is to explicitly point out the mistake in the prior code and request a fix, which accounts for 22.8% of observed error correction cases. Alternative strategies include targeted guiding questions (16.9%) and specific instructions (16.5%), each contributing to the likelihood of resolving coding issues in subsequent LLM responses. These results underscore the practical importance of precise developer signaling—well-crafted prompts help LLMs focus on relevant code segments, reducing error propagation and facilitating iterative quality improvement.

6. Research Applications and Future Directions

CodeChat provides critical empirical evidence for designing and evaluating conversational code assistants. Researchers can utilize the dataset to:

• Analyze the iterative dialogue mechanisms that drive successful code completion and error mitigation.
• Quantify and model the kinds and rates of code defects generated by LLMs across languages and conversation turns.
• Investigate the syntax–context–documentation dynamic as developers incrementally refine prompts and LLMs adapt outputs.
• Develop automated tools for real-time error checking, context management, and adaptive prompt refinement based on multi-turn conversation patterns.

This resource also offers an avenue for benchmarking new code-oriented LLM architectures against real-world conversational behavior, further informing the development of systems that integrate automated code quality assessment and robust dialogue management.

7. Significance for Conversational Programming Systems

The CodeChat dataset reveals that authentic developer–LLM interactions are characterized by verbose, iterative exchanges rather than isolated question–answer patterns. Multi-turn conversations are essential for recovering from LLM output errors, clarifying requirements, and achieving improved code quality, especially in areas such as documentation and dependency handling. Persistent defects—undefined variables, missing headers, insufficient comments—remain a concern, but explicit error-correction prompts and prompt engineering techniques provide pathways for significant improvement.

In summary, CodeChat is a foundational corpus for empirical research on developer–LLM conversational dynamics, error patterns, and iterative coding workflows, offering quantitative and qualitative insights into both the limitations and opportunities for conversational code assistants in contemporary software engineering (Zhong et al., 12 Sep 2025).