New Ideas and Emerging Results Track: Multifractal Aspects of Software Development

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 multiscale self-similarity, as in the case of data drawn from power-law distributions, and quite complex. Specifically we utilize multifractal analysis to demonstrate that software development is multifractal, that is the signal is a fractal with Hurst exponents belonging to a range of fractal dimensions. 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.