research-article

VaryLATEX: Learning Paper Variants That Meet Constraints

Authors:

Jean-Marc Jézéquel,

José A. Galindo,

Jabier Martinez,

Tewfik ZiadiAuthors Info & Claims

VAMOS '18: Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems

Pages 83 - 88

https://doi.org/10.1145/3168365.3168372

Published: 07 February 2018 Publication History

Abstract

How to submit a research paper, a technical report, a grant proposal, or a curriculum vitae that respect imposed constraints such as formatting instructions and page limits? It is a challenging task, especially when coping with time pressure. In this work, we present VaryLATEX, a solution based on variability, constraint programming, and machine learning techniques for documents written in LATEX to meet constraints and deliver on time. Users simply have to annotate LATEX source files with variability information, e.g., (de)activating portions of text, tuning figures' sizes, or tweaking line spacing. Then, a fully automated procedure learns constraints among Boolean and numerical values for avoiding non-acceptable paper variants, and finally, users can further configure their papers (e.g., aesthetic considerations) or pick a (random) paper variant that meets constraints, e.g., page limits. We describe our implementation and report the results of two experiences with VaryLATEX.

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Cited By

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:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111671
Felfernig ALe VLubos S(2023)Conjunctive Query Based Constraint Solving for Feature Model ConfigurationThe 12th Conference on Information Technology and Its Applications10.1007/978-3-031-36886-8_30(357-367)Online publication date: 26-Jul-2023
https://doi.org/10.1007/978-3-031-36886-8_30
Show More Cited By

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VAMOS '18: Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems

February 2018

128 pages

ISBN:9781450353984

DOI:10.1145/3168365

Copyright © 2018 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]

In-Cooperation

Universidad Politécnica de Madrid
URJC: Rey Juan Carlos University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2018

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Refereed limited

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ANR-17-CE25-0010-01

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VAMOS 2018

VAMOS 2018: 12th International Workshop on Variability Modelling of Software-Intensive Systems

February 7 - 9, 2018

Madrid, Spain

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VAMOS '18 Paper Acceptance Rate 15 of 34 submissions, 44%;

Overall Acceptance Rate 66 of 147 submissions, 45%

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Cited By

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:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111671
Felfernig ALe VLubos S(2023)Conjunctive Query Based Constraint Solving for Feature Model ConfigurationThe 12th Conference on Information Technology and Its Applications10.1007/978-3-031-36886-8_30(357-367)Online publication date: 26-Jul-2023
https://doi.org/10.1007/978-3-031-36886-8_30
Popescu APolat-Erdeniz SFelfernig AUta MAtas MLe VPilsl KEnzelsberger MTran T(2022)An overview of machine learning techniques in constraint solvingJournal of Intelligent Information Systems10.1007/s10844-021-00666-558:1(91-118)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10844-021-00666-5
Vavrille MTruchet CPrud’homme C(2022)Solution sampling with random table constraintsConstraints10.1007/s10601-022-09329-w27:4(381-413)Online publication date: 24-Jun-2022
https://doi.org/10.1007/s10601-022-09329-w
Martin HTemple PAcher MPereira JJézéquel J(2022)Machine Learning for Feature Constraints DiscoveryHandbook of Re-Engineering Software Intensive Systems into Software Product Lines10.1007/978-3-031-11686-5_7(175-196)Online publication date: 5-Jul-2022
https://doi.org/10.1007/978-3-031-11686-5_7
Martin HAcher MPereira JJézéquel JMousavi MSchobbens P(2021)A comparison of performance specialization learning for configurable systemsProceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A10.1145/3461001.3471155(46-57)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1145/3461001.3471155
Uta MFelfernig ALe VPopescu ATran THelic DMousavi MSchobbens P(2021)Evaluating recommender systems in feature model configurationProceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A10.1145/3461001.3471144(58-63)Online publication date: 6-Sep-2021
https://dl.acm.org/doi/10.1145/3461001.3471144
Temple PPerrouin GAcher MBiggio BJézéquel JRoli F(2021)Empirical assessment of generating adversarial configurations for software product linesEmpirical Software Engineering10.1007/s10664-020-09915-726:1Online publication date: 12-Jan-2021
https://dl.acm.org/doi/10.1007/s10664-020-09915-7
Pereira JMartin HTemple PAcher MLopez-Herrejon R(2020)Machine learning and configurable systemsProceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A - Volume A10.1145/3382025.3414976(1-1)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3382025.3414976
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