research-article

Towards Feature-Based Analysis of the Machine Learning Development Lifecycle

Authors:

Boyue Caroline Hu,

Marsha ChechikAuthors Info & Claims

ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 2087 - 2091

https://doi.org/10.1145/3611643.3613082

Published: 30 November 2023 Publication History

Abstract

The safety and trustworthiness of systems with components that are based on Machine Learning (ML) require an in-depth understanding and analysis of all stages in its Development Lifecycle (MLDL). High-level abstractions of desired functionalities, model behaviour, and data are called features, and they have been studied by different communities across all MLDL stages. In this paper, we propose to support Software Engineering analysis of the MLDL through features, calling it feature-based analysis of the MLDL. First, to achieve a shared understanding of features among different experts, we establish a taxonomy of existing feature definitions currently used in various MLDL stages. Through this taxonomy, we map features from different stages to each other, discover gaps and future research directions and identify areas of collaboration between Software Engineering and other MLDL experts.

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Cited By

Torbarina LFerkovic TRoguski LMihelcic VSarlija BKraljevic Z(2024)Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Position PaperNatural Language Processing Journal10.1016/j.nlp.2024.1000767(100076)Online publication date: Jun-2024
https://doi.org/10.1016/j.nlp.2024.100076

Index Terms

Towards Feature-Based Analysis of the Machine Learning Development Lifecycle
1. Computing methodologies
  1. Machine learning
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation

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cover image ACM Conferences

ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

November 2023

2215 pages

ISBN:9798400703270

DOI:10.1145/3611643

General Chair:
Satish Chandra
Google, USA
,
Program Chairs:
Kelly Blincoe
University of Auckland, New Zealand
,
Paolo Tonella
USI Lugano, Switzerland

Copyright © 2023 ACM.

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Published: 30 November 2023

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ESEC/FSE '23: 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

December 3 - 9, 2023

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Cited By

Torbarina LFerkovic TRoguski LMihelcic VSarlija BKraljevic Z(2024)Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Position PaperNatural Language Processing Journal10.1016/j.nlp.2024.1000767(100076)Online publication date: Jun-2024
https://doi.org/10.1016/j.nlp.2024.100076

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