Finding self-organized criticality in collaborative work via repository mining

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Presentation of the paper in IWANN2017 titled "Finding self-organized criticality in collaborative work via repository mining"

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Finding self-organized criticality in collaborative work via repository mining

  1. 1. Finding self-organized criticality in collaborative work via repository mining J. J. Merelo1, P. A. Castillo1, Mario García-Valdez2 1 University of Granada (Spain) 2 Instituto Tecnológico de Tijuana (México) 1
  2. 2. 2 Motivation Development teams eventually become complex systems, mainly in collaborative work environments. Relations and collaborations take place through the environment. Pattern mining and analysing social-based information is a complex problem.
  3. 3. 3 Objectives Analysing self-organization in collaborative work environments. Using graphic tools to analyse the dynamics in collaborative work environments. To explore and analyse relations-based data: Do developers self-organize? Contribute to open science tools and methodologies.
  4. 4. 4 Theory In Statistical Physics, criticality is defined as a type of behaviour observed when a system undergoes a phase transition. A state on the edge between two different types of behaviour is called the critical state, and in this state the system is at criticality.
  5. 5. 5 Example: The sandpile model The sandpile model of self-organized criticality: Dropping an additional grain on the pile may set off avalanches that slide down the pile's slopes. Image: h)p://
  6. 6. 6 Small variation, large effect We add one grain to the pile, so in average the steepness of slopes increases. The slopes evolve to a critical state where a single grain of sand is likely to settle on the pile, or to trigger an avalanche. Image: h)ps://
  7. 7. Our aim To present the underlying concepts and ideas from Statistical Physics and nonlinear dynamics that could explain relations in collaborative work environments. Find out the dynamics underlying collaboration and their mechanisms. 7
  8. 8. The study We examined 4 repositories where the collaborative writing of scientific papers take place. Analysing changes in files, looking for the existence of: 1. a scale free structure 2. long-distance correlations 3. pink noise 8
  9. 9. The study In this report we work on a repository for several papers. Repositories with a certain “length”: more than 50 commits (changes) Macro measures extracted from the size of changes. 9
  10. 10. Measures Several macro measures extracted from the size of changes to the files in the repository. •  Sequence of changes •  Timeline of commit sizes •  Change sizes ranked in descending order •  Long-distance correlations •  Presence of pink noise (1/f) 10
  11. 11. 11 Sequence of changes contents static for a long time, followed by big changes (avalanches)
  12. 12. 12 Timeline of commit sizes contents periods with small changes VS other that alternate big and small changes
  13. 13. 13 contents Change sizes ranked in descending order many small changes VS few commits that change many lines.
  14. 14. 14 Long-distance correlations The bird is big autocorrelation is significant if the lines go over the mean (dashed line)
  15. 15. 15 text the spectrum should present a slope equal to -1 There is not a clear trend downwards. The presence of pink noise is not as clear as the other two characteristics Presence of pink noise, as measured by the power spectral density (1/f)
  16. 16. 16 Conclusions After analysing several repositories for scientific papers, they are in a critical state: •  changes have a scale-free form, and •  there are long-distance correlations •  pink noise (only in some cases) Open Science + reproducibility: draw your own conclusions using the programs and data published at: “Measuring progress in literature and in other creative endeavours, like programming”
  17. 17. Any question? Pedro A. Castillo University of Granada 17