Dealing with software complexity in individual‐based models

Daniel Vedder*, Markus Ankenbrand, Juliano Sarmento Cabral

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

1. Individual-based models are doubly complex: as well as representing complex ecological systems, the software that implements them is complex in itself. Both forms of complexity must be managed to create reliable models. However, the ecological modelling literature to date has focussed almost exclusively on the biological complexity. Here, we discuss methods for containing software complexity.
2. Strategies for containing complexity include avoiding, subdividing, documenting and reviewing it. Computer science has long-established techniques for all of these strategies. We present some of these techniques and set them in the context of IBM development, giving examples from published models.
3. Techniques for avoiding software complexity are following best practices for coding style, choosing suitable programming languages and file formats and setting up an automated workflow. Complex software systems can be made more tractable by encapsulating individual subsystems. Good documentation needs to take into account the perspectives of scientists, users and developers. Code reviews are an effective way to check for errors, and can be used together with manual or automated unit and integration tests.
4. Ecological modellers can learn from computer scientists how to deal with complex software systems. Many techniques are readily available, but must be disseminated among modellers. There is a need for further work to adapt software development techniques to the requirements of academic research groups and individual-based modelling.
Original languageEnglish
Pages (from-to)2324-2333
Number of pages8
JournalMethods in Ecology and Evolution
Volume12
Issue number12
Early online date6 Sept 2021
DOIs
Publication statusPublished - Dec 2021

Keywords

  • ecological modelling
  • individual-based models
  • model complexity
  • research software engineering
  • software complexity
  • software development

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