Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2023

Abstract

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This dataset can help researchers understand the practices that are adopted in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

Keywords

Daily lives, Engineered software project, High quality, Labeled dataset, Learning projects, Machine-learning, Open source platforms, Open source projects, Software engineering practices, Software project

Discipline

Computer and Systems Architecture | Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering

Publication

Proceedings of the 20th IEEE/ACM International Conference on Mining Software Repositories, Melbourne, Australia, May 15-16

First Page

62

Last Page

66

ISBN

9798350311846

Identifier

10.1109/MSR59073.2023.00022

Publisher

IEEE

City or Country

New Jersey

Additional URL

https://doi.org/10.1109/MSR59073.2023.00022

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