A Tool for Automatically Cataloguing and Selecting Pre-Trained Models and Datasets for Software Engineering

TL;DR

MLAssetSelection is a web tool that automates the discovery, ranking, and updating of machine learning models and datasets tailored for software engineering tasks, simplifying asset selection for engineers.

Contribution

It introduces a novel web application that automatically extracts, ranks, and updates SE-specific ML assets, enhancing efficiency and personalization in asset selection.

Findings

Provides real-time updates of SE assets

Enables requirement-based model and dataset selection

Offers a configurable leaderboard for benchmarking

Abstract

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.