VaryLATEX: Learning Paper Variants That Meet Constraints

Mathieu Acher, Paul Temple, Jean-Marc Jézéquel, José Angel Galindo Duarte, Jabier Martinez, Tewfik Ziadi

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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 VaryL ATEX, a solution based on variability, constraint programming, and machine learning techniques for documents written in L ATEX to meet constraints and deliver on time. Users simply have to annotate L ATEX 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 VaryL ATEX.

Original languageEnglish
Title of host publicationProceedings - VaMoS 2018
Subtitle of host publication12th International Workshop on Variability Modelling of Software-Intensive Systems
EditorsMalte Lochau, Rafael Capilla
Pages83-88
Number of pages6
ISBN (Electronic)9781450353984
DOIs
Publication statusPublished - 7 Feb 2018

Publication series

NameACM International Conference Proceeding Series

Keywords

  • Constraint programming
  • Generators
  • L TEX
  • Machine learning
  • Technical writing
  • Variability modelling

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