tutorial

Machine learning and configurable systems: a gentle introduction

Authors:

Juliana Alves Pereira,

Hugo Martin,

Paul Temple,

Mathieu AcherAuthors Info & Claims

SPLC '20: Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A - Volume A

Article No.: 40, Page 1

https://doi.org/10.1145/3382025.3414976

Published: 19 October 2020 Publication History

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Abstract

The goal of this tutorial is to give a gentle introduction to how machine learning can be used to support software product line configuration. This is our second practical tutorial in this trending field. The tutorial is based on a systematic literature review and includes practical tasks (specialization, performance and bug prediction) on real-world systems (Linux, VaryLaTeX, x264). The material is designed for academics and practitioners with basic knowledge in software product lines and machine learning.

References

[1]

Mathieu Acher, Hugo Martin, Juliana Alves Pereira, Arnaud Blouin, Jean-Marc Jézéquel, Djamel Khelladi, Luc Lesoil, and Olivier Barais. 2019. Learning Very Large Configuration Spaces: What Matters for Linux Kernel Sizes. (2019).

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[2]

Mathieu Acher, Paul Temple, Jean-Marc Jézéquel, José A. Galindo, Jabier Martinez, and Tewfik Ziadi. 2018. VaryLATEX: Learning Paper Variants That Meet Constraints. In VAMOS. 83--88.

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[3]

Juliana Alves Pereira, Mathieu Acher, Hugo Martin, and Jean-Marc Jézéquel. 2020. Sampling Effect on Performance Prediction of Configurable Systems: A Case Study. In ACM/SPEC ICPE. 277--288.

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[4]

Juliana Alves Pereira, Hugo Martin, Mathieu Acher, Jean-Marc Jézéquel, Goetz Botterweck, and Anthony Ventresque. 2019. Learning Software Configuration Spaces: A Systematic Literature Review. (2019).

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Cited By

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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

Index Terms

Machine learning and configurable systems: a gentle introduction
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
        Software product lines

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Information

Published In

SPLC '20: Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A - Volume A

October 2020

323 pages

ISBN:9781450375696

DOI:10.1145/3382025

General Chair:
Roberto Erick Lopez-Herrejon
École de technologie supérieure, Canada

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.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2020

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Tutorial

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CAPES
ANR

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SPLC '20

Sponsor:

SIGSOFT

SPLC '20: 24th ACM International Systems and Software Product Line Conference

October 19 - 23, 2020

Quebec, Montreal, Canada

Acceptance Rates

SPLC '20 Paper Acceptance Rate 17 of 49 submissions, 35%;

Overall Acceptance Rate 167 of 463 submissions, 36%

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Cited By

View all

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

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Machine Learning and Configurable Systems: A Gentle Introduction

Learning software configuration spaces: A systematic literature review

Machine Learning: The State of the Art