优化代码补全的上下文收集，提升大项目中Python与Kotlin的补全质量。
Challenge on Optimization of Context Collection for Code Completion
- 设计高效机制从代码库提取上下文信息。
- 在Python和Kotlin上实现显著更高的chrF得分。
- 适合关注AI辅助编程与代码生成的研究者。
随着AI在软件工程中的快速发展，系统评估其利用大型代码库中项目级信息的能力变得尤为重要。本次由JetBrains与Mistral AI联合举办的ASE 2025会议挑战赛，聚焦于代码补全中的上下文收集优化，参赛者需针对Python与Kotlin开发高效上下文获取机制，以提升‘填空式’代码补全质量。研究基于宽松许可协议的开源项目构建了大规模真实代码数据集。评估基于chrF指标，衡量多个先进神经模型的补全表现。公开阶段有19支队伍提交Python方案，8支提交Kotlin方案；私密阶段6支队伍参与，其中5支提交论文至研讨会。
原文摘要 · Abstract (English)
The rapid advancement of workflows and methods for software engineering using AI emphasizes the need for a systematic evaluation and analysis of their ability to leverage information from entire projects, particularly in large code bases. In this challenge on optimization of context collection for code completion, organized by JetBrains in collaboration with Mistral AI as part of the ASE 2025 conference, participants developed efficient mechanisms for collecting context from source code repositories to improve fill-in-the-middle code completions for Python and Kotlin. We constructed a large dataset of real-world code in these two programming languages using permissively licensed open-source projects. The submissions were evaluated based on their ability to maximize completion quality for multiple state-of-the-art neural models using the chrF metric. During the public phase of the competition, nineteen teams submitted solutions to the Python track and eight teams submitted solutions to the Kotlin track. In the private phase, six teams competed, of which five submitted papers to the workshop.
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