Multifractal Aspects of Software Development

• Author(s) / Creator(s)

  • Hindle, Abram
  • Godfrey, M.M.
  • Holt, R.C.

• Software development is difficult to model, particularly the noisy, non-stationary signals of changes per time unit, extracted from version control systems (VCSs). Currently researchers are utilizing timeseries analysis tools such as ARIMA to model these signals extracted from a project's VCS. Unfortunately current approaches are not very amenable to the underlying power-law distributions of this kind of signal. We propose modeling changes per time unit using multifractal analysis. This analysis can be used when a signal exhibits multi-scale self-similarity, as in the case of complex data drawn from power-law distributions. Specifically we utilize multifractal analysis to demonstrate that software development is multifractal, that is the signal is a fractal composed of multiple fractal dimensions along a range of Hurst exponents. Thus we show that software development has multi-scale self-similarity, that software development is multifractal. We also pose questions that we hope multifractal analysis can answer.

• Date created

  2011

• Subjects / Keywords

  • multifractal
  • fractal
  • version control
  • wavelets
  • power-law

• Type of Item

  Conference/Workshop Presentation

• DOI

  https://doi.org/10.7939/r3-hgn0-2q09

• License

  Attribution 4.0 International