CursorCore: Assist Programming through Aligning Anything

TL;DR

CursorCore introduces a comprehensive conversational framework for programming assistance that integrates multiple information sources, along with a new benchmark and a large dataset, to improve code-related tasks.

Contribution

The paper presents a novel framework, benchmark, and data pipeline for enhancing programming assistance models through better information alignment and automation.

Findings

CursorCore outperforms comparable models in programming tasks.

The new benchmark APEval effectively assesses model alignment and output quality.

Generated 219K training samples to fine-tune models and demonstrate improvements.

Abstract

Large language models have been successfully applied to programming assistance tasks, such as code completion, code insertion, and instructional code editing. However, these applications remain insufficiently automated and struggle to effectively integrate various types of information during the programming process, including coding history, current code, and user instructions. In this work, we propose a new conversational framework that comprehensively integrates these information sources, collect data to train our models and evaluate their performance. Firstly, to thoroughly evaluate how well models align with different types of information and the quality of their outputs, we introduce a new benchmark, APEval (Assist Programming Eval), to comprehensively assess the performance of models in programming assistance tasks. Then, for data collection, we develop a data generation pipeline, Programming-Instruct, which synthesizes training data from diverse sources, such as GitHub and online judge platforms. This pipeline can automatically generate various types of messages throughout the programming process. Finally, using this pipeline, we generate 219K samples, fine-tune multiple models, and develop the CursorCore series. We show that CursorCore outperforms other models of comparable size. This framework unifies applications such as inline chat and automated editing, contributes to the advancement of coding assistants. Code, models and data are freely available at https://github.com/TechxGenus/CursorCore.