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Conversational AI for Programming: Aligning Anything During Code Development

New research proposes a conversational framework that uses a large language model (LLM) to help developers write code by learning from all the information available during the development process, including code history, current code, and user instructions. The paper proposes a new framework called Assistant-Conversation to address the limitations of current programming assistants. The authors show that their approach leads to a better performance for tasks like code completion, code insertion, and code editing.

Current programming assistance methods typically focus on either completing or inserting code snippets based on the current code context or following instructions given in natural language. This leads to limitations, as developers often need to edit code instead of just completing or inserting snippets. Moreover, writing detailed prompts for specific tasks can be time-consuming and challenging.

Assistant-Conversation proposes a new way of interacting with LLMs for programming assistance. It utilizes all the information available during the development process, including code history, current code, and user instructions, to predict the required changes and generate a more comprehensive output. The authors developed a benchmark called APEval to evaluate the alignment of models with different types of information and the quality of their outputs. APEval also includes diverse types of information, including code snippets, user instructions, and chat-style interactions, to assess the performance of models in more realistic scenarios.

The authors also developed a data generation pipeline called Programming-Instruct to synthesize training data from various sources, such as GitHub, online judge platforms, and LLMs. This data generation method can automatically produce any type of messages throughout the programming process without requiring additional human annotation.

By utilizing Programming-Instruct, the authors generated 219K data points to fine-tune multiple models and develop the CursorCore series. The CursorCore series outperforms other models of comparable size in both the 1B+ and 6B+ parameter sizes. This shows that the framework effectively utilizes all available information to assist developers in a more comprehensive and efficient way.

The paper highlights the importance of considering all available information during code development. By leveraging the full context of the development process, the model can generate more accurate and relevant predictions. This approach has the potential to significantly improve the efficiency and effectiveness of programming assistance tools. In the future, the researchers plan to explore expanding this approach to support models capable of assisting with repository-level development and other applications.