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.

References

[1]

Mathieu Acher, Guillaume Bécan, Benoit Combemale, Benoit Baudry, and Jean-Marc Jézéquel. 2015. Product lines can jeopardize their trade secrets. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE'15). 930--933.

Digital Library

[2]

Mathieu Acher, Philippe Collet, Philippe Lahire, and Robert France. 2013. FAMILIAR: A Domain-Specific Language for Large Scale Management of Feature Models. Science of Computer Programming (SCP) 78, 6 (2013), 657--681.

Digital Library

[3]

David Benavides, Sergio Segura, and Antonio Ruiz-Cortés. 2010. Automated analysis of feature models 20 years later: a literature review. Information Systems 35, 6 (2010), 615--708.

Digital Library

[4]

Danilo Beuche. 2010. Modeling and Building Software Product Lines with pure::variants. In SPLC Workshops. 296.

Digital Library

[5]

Jianmei Guo, Krzysztof Czarnecki, Sven Apel, Norbert Siegmund, and Andrzej Wasowski. 2013. Variability-aware performance prediction: A statistical learning approach. In ASE.

Digital Library

[6]

Mikolás Janota, Goetz Botterweck, and João Marques-Silva. 2014. On lazy and eager interactive reconfiguration. In The Eighth International Workshop on Variability Modelling of Software-intensive Systems, VaMoS '14, Sophia Antipolis, France, January 22-24, 2014. 8:1--8:8.

Digital Library

[7]

Christian Kästner, Thomas Thüm, Gunter Saake, Janet Feigenspan, Thomas Leich, Fabian Wielgorz, and Sven Apel. 2009. FeatureIDE: A tool framework for feature-oriented software development. In ICSE. IEEE, 611--614.

Digital Library

[8]

Ebrahim Khalil Abbasi, Arnaud Hubaux, Mathieu Acher, Quentin Boucher, and Patrick Heymans. 2013. The Anatomy of a Sales Configurator: An Empirical Study of 111 Cases. In CAiSE'13.

[9]

Charles W. Krueger and Paul C. Clements. {n. d.}. Systems and software product line engineering with BigLever software gears. In SPLC 2012: Volume 2.

Digital Library

[10]

Wei-Yin Loh. 2011. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, 1 (2011), 14--23.

[11]

Jabier Martinez, Jean-Sebastien Sottet, Alfonso Garcia Frey, Tewfik Ziadi, Tegawende Bissyande, Jean Vanderdonckt, Jacques Klein, and Yves Le Traon. 2017. Variability Management and Assessment for User Interface Design. In Human Centered Software Product Lines, Alfonso Garcia Frey Jean-Sebastien Sottet and Jean Vanderdonckt (Eds.). Springer International Publishing, 81--106.

[12]

A. Sarkar, Jianmei Guo, N. Siegmund, S. Apel, and K. Czarnecki. 2015. Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T). In ASE'15.

[13]

Norbert Siegmund, Marko RosenmüLler, Christian KäStner, Paolo G. Giarrusso, Sven Apel, and Sergiy S. Kolesnikov. 2013. Scalable Prediction of Non-functional Properties in Software Product Lines: Footprint and Memory Consumption. Inf. Softw. Technol. (2013).

Digital Library

[14]

Paul Temple, Mathieu Acher, Jean-Marc Jézéquel, and Olivier Barais. 2017. Learning-Contextual Variability Models. IEEE Software (nov 2017). https://hal.inria.fr/hal-01659137

[15]

Paul Temple, Mathieu Acher, Jean-Marc A Jézéquel, Léo A Noel-Baron, and José A Galindo. 2017. Learning-Based Performance Specialization of Configurable Systems. Research Report. IRISA, Inria Rennes; University of Rennes 1. https://hal.archives-ouvertes.fr/hal-01467299

[16]

Paul Temple, José A. Galindo, Mathieu Acher, and Jean-Marc Jézéquel. 2016. Using Machine Learning to Infer Constraints for Product Lines. In Proceedings of the 20th International Systems and Software Product Line Conference (SPLC '16). ACM, New York, NY, USA, 209--218.

Digital Library

[17]

Pavel Valov, Jianmei Guo, and Krzysztof Czarnecki. {n. d.}. Empirical comparison of regression methods for variability-aware performance prediction. In SPLC'15.

Digital Library

[18]

Yingfei Xiong, Arnaud Hubaux, Steven She, and Krzysztof Czarnecki. 2012. Generating Range Fixes for Software Configuration. In 34th International Conference on Software Engineering.

Digital Library

[19]

Yi Zhang, Jianmei Guo, Eric Blais, and Krzysztof Czarnecki. {n. d.}. Performance Prediction of Configurable Software Systems by Fourier Learning (T). In ASE'15.

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

Recommendations

Partially defined constraints in constraint-based design

In constraint-based design, components are modeled by variables describing their properties and subject to physical or mechanical constraints. However, some other constraints are difficult to represent, like comfort or user satisfaction. Partially ...
LaTeX: when word fails you
SIGUCCS '05: Proceedings of the 33rd annual ACM SIGUCCS conference on User services

Communicating in an academic environment requires an appropriate choice of software for content delivery. Microsoft Word can be used to successfully produce documents, for a variety of needs, but has some limitations. For these occasions, TeX is an ...
Learning constraints in spreadsheets and tabular data

Spreadsheets, comma separated value files and other tabular data representations are in wide use today. However, writing, maintaining and identifying good formulas for tabular data and spreadsheets can be time-consuming and error-prone. We investigate ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

ANR-17-CE25-0010-01

Conference

VAMOS 2018

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

February 7 - 9, 2018

Madrid, Spain

Acceptance Rates

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

Overall Acceptance Rate 66 of 147 submissions, 45%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
101
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)2

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents