research-article

Using machine learning to infer constraints for product lines

Authors:

José A. Galindo,

Jean-Marc JézéquelAuthors Info & Claims

SPLC '16: Proceedings of the 20th International Systems and Software Product Line Conference

Pages 209 - 218

https://doi.org/10.1145/2934466.2934472

Published: 16 September 2016 Publication History

Abstract

Variability intensive systems may include several thousand features allowing for an enormous number of possible configurations, including wrong ones (e.g. the derived product does not compile). For years, engineers have been using constraints to a priori restrict the space of possible configurations, i.e. to exclude configurations that would violate these constraints. The challenge is to find the set of constraints that would be both precise (allow all correct configurations) and complete (never allow a wrong configuration with respect to some oracle). In this paper, we propose the use of a machine learning approach to infer such product-line constraints from an oracle that is able to assess whether a given product is correct. We propose to randomly generate products from the product line, keeping for each of them its resolution model. Then we classify these products according to the oracle, and use their resolution models to infer cross-tree constraints over the product-line. We validate our approach on a product-line video generator, using a simple computer vision algorithm as an oracle. We show that an interesting set of cross-tree constraint can be generated, with reasonable precision and recall.

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Cited By

Gomez-Vazquez MCabot J(2024)Exploring the Use of Software Product Lines for the Combination of Machine Learning ModelsProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3676599(26-29)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3676599
Acher M(2024)A Demonstration of End-User Code Customization Using Generative AIProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3634713.3634732(139-145)Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1145/3634713.3634732
Klikovits SGambi ADhungana DRabiser R(2024)Leveraging Software Product Lines for Testing Autonomous VehiclesProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3634713.3634720(56-60)Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1145/3634713.3634720
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cover image ACM Other conferences

SPLC '16: Proceedings of the 20th International Systems and Software Product Line Conference

September 2016

367 pages

ISBN:9781450340502

DOI:10.1145/2934466

General Chair:
Hong Mei
Shanghai Jiao Tong University, China

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Huawei Technologies Co. Ltd.: Huawei Technologies Co. Ltd.
Key Laboratory of High Confidence Software Technologies: Key Laboratory of High Confidence Software Technologies, Ministry of Education
DC Holdings: Digital China Holdings Limited

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2016

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SPLC '16

Sponsor:

Huawei Technologies Co. Ltd.
Key Laboratory of High Confidence Software Technologies
DC Holdings

SPLC '16: The 20th International Systems and Software Product Line Conference

September 16 - 23, 2016

Beijing, China

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Cited By

Gomez-Vazquez MCabot J(2024)Exploring the Use of Software Product Lines for the Combination of Machine Learning ModelsProceedings of the 28th ACM International Systems and Software Product Line Conference10.1145/3646548.3676599(26-29)Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1145/3646548.3676599
Acher M(2024)A Demonstration of End-User Code Customization Using Generative AIProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3634713.3634732(139-145)Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1145/3634713.3634732
Klikovits SGambi ADhungana DRabiser R(2024)Leveraging Software Product Lines for Testing Autonomous VehiclesProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3634713.3634720(56-60)Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1145/3634713.3634720
Böhm SKrieter SHeß TThüm TLochau M(2024)Incremental Identification of T-Wise Feature InteractionsProceedings of the 18th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3634713.3634715(27-36)Online publication date: 7-Feb-2024
https://dl.acm.org/doi/10.1145/3634713.3634715
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
Stötzner MKlinaku FPesl RBecker SFu SCarnevale LRana OVillari M(2023)Enhancing Deployment Variability Management by Pruning Elements in Deployment ModelsProceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing10.1145/3603166.3632143(1-11)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3603166.3632143
Bazin AHuchard MMartin P(2023)Towards Analyzing Variability in Space and Time of Products from a Product Line using Triadic Concept AnalysisProceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B10.1145/3579028.3609019(85-89)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1145/3579028.3609019
Temple PPerrouin G(2023)Explicit or Implicit? On Feature Engineering for ML-based Variability-intensive SystemsProceedings of the 17th International Working Conference on Variability Modelling of Software-Intensive Systems10.1145/3571788.3571804(91-93)Online publication date: 25-Jan-2023
https://dl.acm.org/doi/10.1145/3571788.3571804
Lesoil LAcher MBlouin AJézéquel J(2023)Input sensitivity on the performance of configurable systems an empirical studyJournal of Systems and Software10.1016/j.jss.2023.111671201(111671)Online publication date: Jul-2023
https://doi.org/10.1016/j.jss.2023.111671
Horcas JGalindo JHeradio RFernandez-Amoros DBenavides D(2023)A Monte Carlo tree search conceptual framework for feature model analysesJournal of Systems and Software10.1016/j.jss.2022.111551195:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111551
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