VaryLaTeX has been fully rewritten in Python and is now available here, with many new features (just drop your archive into your Web browser): https://github.com/diverse-project/varylatex
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, isn't it? VaryLaTeX is a solution based on variability, constraint programming , and machine learning techniques for documents written in LaTeX to meet constraints and deliver on time.
As a user, you 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 are working on an integrated, lightweight, and usable solution. Feel free to contribute, suggest features, provide feedbacks, use cases
More details can be found in the following paper, published/presented at 12th International Workshop on Variability Modelling of Software-Intensive Systems https://vamos2018.wordpress.com/: "VaryLaTeX: Learning Paper Variants That Meet Constraints" by Mathieu Acher, Paul Temple, Jean-Marc Jézéquel, José A. Galindo, Jabier Martinez, Tewfik Ziadi: https://hal.inria.fr/hal-01659161/
Screencast (demonstration performed in 2015, no sound unfortunately): https://www.youtube.com/watch?v=n9pdUddr5m4
The code is mainly written in Java with a bunch of bash/R scripts We are using Mustache for the templating engine (https://mustache.github.io/ and specifically on Trimou: http://trimou.org/) and Choco for the solving part (http://www.choco-solver.org/). You also need FAMILIAR: https://github.com/FAMILIAR-project/familiar-language
This work benefited from the support of the project ANR-17-CE25-0010-01 VaryVary. We are seeking candidates for working around the topic of machine learning and configurable systems (VaryLaTeX is an interesting case of VaryVary): https://docs.google.com/document/d/1Vr8HByYefWDRDdVeMtToXtpauFwcxxQeXLZtsX7T1UI/edit?usp=sharing