The rapid growth of machine learning assets has made it increasingly difficult for software engineers to identify models and datasets that match their specific needs. Browsing large registries, such as Hugging Face, is time-consuming, error-prone, and rarely tailored to Software Engineering (SE) tasks. We present MLAssetSelection, a web application that automatically extracts SE assets and supports four key functionalities: (i) a configurable leaderboard for ranking models across multiple benchmarks and metrics; (ii) requirements-based selection of models and datasets; (iii) real-time automated updates through scheduled jobs that keep asset information current; and (iv) user-centric features including login, personalized asset lists, and configurable alert notifications. A demonstration video is available at https://youtu.be/t6CJ6P9asV4.

翻译：机器学习资产的快速增长使得软件工程师越来越难以识别符合其特定需求的模型与数据集。浏览大型注册库（如Hugging Face）耗时费力、易出错，且很少针对软件工程（SE）任务进行定制。本文提出MLAssetSelection——一个能自动提取SE资产并支持四项核心功能的Web应用程序：（i）可配置的排行榜，支持跨多基准测试与指标对模型进行排序；（ii）基于需求的模型与数据集选择；（iii）通过定时任务实现实时自动更新，确保资产信息时效性；（iv）以用户为中心的功能，包括登录、个性化资产列表及可配置的警报通知。演示视频详见：https://youtu.be/t6CJ6P9asV4。