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FeatureNET: diversity-driven generation of deep learning models

Authors:

Salah Ghamizi,

Maxime Cordy,

Mike Papadakis,

Yves Le TraonAuthors Info & Claims

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings

Pages 41 - 44

https://doi.org/10.1145/3377812.3382153

Published: 01 October 2020 Publication History

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Abstract

We present FeatureNET, an open-source Neural Architecture Search (NAS) tool¹ that generates diverse sets of Deep Learning (DL) models. FeatureNET relies on a meta-model of deep neural networks, consisting of generic configurable entities. Then, it uses tools developed in the context of software product lines to generate diverse (maximize the differences between the generated) DL models. The models are translated to Keras and can be integrated into typical machine learning pipelines. FeatureNET allows researchers to generate seamlessly a large variety of models. Thereby, it helps choosing appropriate DL models and performing experiments with diverse models (mitigating potential threats to validity). As a NAS method, FeatureNET successfully generates models performing equally well with handcrafted models.

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Fortz STemple PDevroey XHeymans PPerrouin GAmpatzoglou AFeitosa DCatolino GLenarduzzi V(2021)VaryMinions: leveraging RNNs to identify variants in event logsProceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution10.1145/3472674.3473980(13-18)Online publication date: 23-Aug-2021
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Index Terms

FeatureNET: diversity-driven generation of deep learning models
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings

June 2020

357 pages

ISBN:9781450371223

DOI:10.1145/3377812

General Chairs:
Gregg Rothermel
North Carolina State University
,
Doo-Hwan Bae
KAIST, South Korea

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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KIISE: Korean Institute of Information Scientists and Engineers
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Published: 01 October 2020

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ICSE '20: 42nd International Conference on Software Engineering

June 27 - July 19, 2020

Seoul, South Korea

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Shahra EWu WBasurra SAneiba A(2024)Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution SystemWater10.3390/w1602019616:2(196)Online publication date: 5-Jan-2024
https://doi.org/10.3390/w16020196
Fortz STemple PDevroey XHeymans PPerrouin G(2024)VaryMinions: leveraging RNNs to identify variants in variability-intensive systems’ logsEmpirical Software Engineering10.1007/s10664-024-10473-529:4Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1007/s10664-024-10473-5
Fortz STemple PDevroey XHeymans PPerrouin GAmpatzoglou AFeitosa DCatolino GLenarduzzi V(2021)VaryMinions: leveraging RNNs to identify variants in event logsProceedings of the 5th International Workshop on Machine Learning Techniques for Software Quality Evolution10.1145/3472674.3473980(13-18)Online publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1145/3472674.3473980
Giray G(2021)A software engineering perspective on engineering machine learning systems: State of the art and challengesJournal of Systems and Software10.1016/j.jss.2021.111031(111031)Online publication date: Jun-2021
https://doi.org/10.1016/j.jss.2021.111031

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